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John Shawe-Taylor
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- affiliation: University College, London, UK
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2020 – today
- 2025
- [j110]Dino Pedreschi
, Luca Pappalardo
Human-AI coevolution. Artif. Intell. 339: 104244 (2025) - 2024
- [j109]Nicolas Belissent, José M. Peña
Transfer and zero-shot learning for scalable weed detection and classification in UAV images. Knowl. Based Syst. 292: 111586 (2024) - [c155]Yuxiang Qiu
A Toolbox for Modelling Engagement with Educational Videos. AAAI 2024: 23128-23136 - [i59]Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela:
A Toolbox for Modelling Engagement with Educational Videos. CoRR abs/2401.05424 (2024) - [i58]Yuta Nagano, Andrew Pyo, Martina Milighetti, James Henderson, John Shawe-Taylor, Benny Chain, Andreas Tiffeau-Mayer:
Contrastive learning of T cell receptor representations. CoRR abs/2406.06397 (2024) - 2023
- [j108]Kevin Baum
From fear to action: AI governance and opportunities for all. Frontiers Comput. Sci. 5 (2023) - [j107]Jamie Danemayer
Seeking information about assistive technology: Exploring current practices, challenges, and the need for smarter systems. Int. J. Hum. Comput. Stud. 177: 103078 (2023) - [j106]Jie M. Zhang, Mark Harman, Benjamin Guedj
Model validation using mutated training labels: An exploratory study. Neurocomputing 539: 126116 (2023) - [c154]Simon Schmitt, John Shawe-Taylor
Exploration via Epistemic Value Estimation. AAAI 2023: 9742-9751 - [e12]Paul Lukowicz, Sven Mayer, Janin Koch, John Shawe-Taylor, Ilaria Tiddi:
HHAI 2023: Augmenting Human Intellect - Proceedings of the Second International Conference on Hybrid Human-Artificial Intelligence, June 26-30, 2023, Munich, Germany. Frontiers in Artificial Intelligence and Applications 368, IOS Press 2023, ISBN 978-1-64368-394-2 [contents] - [i57]Simon Schmitt, John Shawe-Taylor, Hado van Hasselt:
Exploration via Epistemic Value Estimation. CoRR abs/2303.04012 (2023) - [i56]Dino Pedreschi, Luca Pappalardo, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott
Social AI and the Challenges of the Human-AI Ecosystem. CoRR abs/2306.13723 (2023) - [i55]Theodore Wolf, Nantas Nardelli, John Shawe-Taylor, María Pérez-Ortiz:
Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models. CoRR abs/2312.06527 (2023) - 2022
- [j105]Shiliang Sun
Stability-based PAC-Bayes analysis for multi-view learning algorithms. Inf. Fusion 86-87: 76-92 (2022) - [c153]Simon Schmitt, John Shawe-Taylor, Hado van Hasselt:
Chaining Value Functions for Off-Policy Learning. AAAI 2022: 8187-8195 - [c152]María Pérez-Ortiz, Sahan Bulathwela
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? CHIIR 2022: 90-101 - [c151]Sahan Bulathwela, Meghana Verma, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. EDM 2022 - [c150]Florina-Cristina Calnegru
Correlation Based Semantic Transfer with Application to Domain Adaptation. ICONIP (1) 2022: 588-599 - [i54]María Pérez-Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, Emine Yilmaz:
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? CoRR abs/2201.03408 (2022) - [i53]Simon Schmitt, John Shawe-Taylor, Hado van Hasselt:
Chaining Value Functions for Off-Policy Learning. CoRR abs/2201.06468 (2022) - [i52]Reuben Adams, John Shawe-Taylor, Benjamin Guedj:
Controlling Confusion via Generalisation Bounds. CoRR abs/2202.05560 (2022) - [i51]Najiba Toron, Janaina Mourão Miranda, John Shawe-Taylor:
TransductGAN: a Transductive Adversarial Model for Novelty Detection. CoRR abs/2203.15406 (2022) - [i50]Sahan Bulathwela
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. CoRR abs/2207.01504 (2022) - [i49]Wendy E. Mackay, John Shawe-Taylor, Frank van Harmelen:
Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262). Dagstuhl Reports 12(6): 112-117 (2022) - 2021
- [j104]Maxime Haddouche, Benjamin Guedj
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. Entropy 23(10): 1330 (2021) - [j103]María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári:
Tighter Risk Certificates for Neural Networks. J. Mach. Learn. Res. 22: 227:1-227:40 (2021) - [j102]Arthur Gwagwa
Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. Patterns 2(12): 100381 (2021) - [c149]María Pérez-Ortiz, Claire Dormann, Yvonne Rogers, Sahan Bulathwela
X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI. IUI Companion 2021: 70-74 - [i48]Sahan Bulathwela, María Pérez-Ortiz, Erik Novak, Emine Yilmaz, John Shawe-Taylor:
PEEK: A Large Dataset of Learner Engagement with Educational Videos. CoRR abs/2109.03154 (2021) - [i47]María Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler:
Learning PAC-Bayes Priors for Probabilistic Neural Networks. CoRR abs/2109.10304 (2021) - [i46]María Pérez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor:
Progress in Self-Certified Neural Networks. CoRR abs/2111.07737 (2021) - [i45]María Pérez-Ortiz, Erik Novak, Sahan Bulathwela, John Shawe-Taylor:
An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All. CoRR abs/2112.01242 (2021) - [i44]Sahan Bulathwela, María Pérez-Ortiz, Catherine Holloway, John Shawe-Taylor:
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution. CoRR abs/2112.02034 (2021) - [i43]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems. CoRR abs/2112.04368 (2021) - 2020
- [j101]Luca Oneto
Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing 416: 231-243 (2020) - [j100]Sahan Bulathwela, María Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin de la Higuera, John Shawe-Taylor, Emine Yilmaz:
Report on the WSDM 2020 workshop on state-based user modelling (SUM'20). SIGIR Forum 54(1): 5:1-5:11 (2020) - [c148]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. AAAI 2020: 565-573 - [c147]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract). AAAI 2020: 13759-13760 - [c146]Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor:
Predicting Engagement in Video Lectures. EDM 2020 - [c145]Tobias Baumann, Thore Graepel, John Shawe-Taylor
Adaptive Mechanism Design: Learning to Promote Cooperation. IJCNN 2020: 1-7 - [c144]Jun Yamada, John Shawe-Taylor
Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning. IJCNN 2020: 1-8 - [c143]Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor:
PAC-Bayes Analysis Beyond the Usual Bounds. NeurIPS 2020 - [c142]Sahan Bulathwela
SUM'20: State-based User Modelling. WSDM 2020: 899-900 - [i42]Jun Yamada, John Shawe-Taylor, Zafeirios Fountas:
Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning. CoRR abs/2002.03267 (2020) - [i41]Yuxin Sun, Benny Chain, Samuel Kaski, John Shawe-Taylor:
Correlated Feature Selection with Extended Exclusive Group Lasso. CoRR abs/2002.12460 (2020) - [i40]Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor:
Predicting Engagement in Video Lectures. CoRR abs/2006.00592 (2020) - [i39]Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor:
PAC-Bayes unleashed: generalisation bounds with unbounded losses. CoRR abs/2006.07279 (2020) - [i38]Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor:
PAC-Bayes Analysis Beyond the Usual Bounds. CoRR abs/2006.13057 (2020) - [i37]María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári:
Tighter risk certificates for neural networks. CoRR abs/2007.12911 (2020) - [i36]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement. CoRR abs/2011.02273 (2020) - [i35]Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor:
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings. CoRR abs/2012.03780 (2020) - [i34]Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor:
Upper and Lower Bounds on the Performance of Kernel PCA. CoRR abs/2012.10369 (2020)
2010 – 2019
- 2019
- [j99]Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage 195: 215-231 (2019) - [i33]Jie M. Zhang
Perturbed Model Validation: A New Framework to Validate Model Relevance. CoRR abs/1905.10201 (2019) - [i32]Petru Manescu, Lydia Neary-Zajiczek, Michael J. Shaw, Muna Elmi, Remy Claveau, Vijay Pawar, John Shawe-Taylor, Iasonas Kokkinos, Mandayam A. Srinivasan, Ikeoluwa Lagunju, Olugbemiro Sodeinde, Biobele J. Brown, Delmiro Fernandez-Reyes:
Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy. CoRR abs/1906.07496 (2019) - [i31]Biobele J. Brown, Alexander A. Przybylski, Petru Manescu, Fabio Caccioli, Gbeminiyi Oyinloye, Muna Elmi, Michael J. Shaw, Vijay Pawar, Remy Claveau, John Shawe-Taylor, Mandayam A. Srinivasan, Nathaniel K. Afolabi, Adebola E. Orimadegun, Wasiu A. Ajetunmobi, Francis Akinkunmi, Olayinka Kowobari, Kikelomo Osinusi, Felix O. Akinbami, Samuel Omokhodion, Wuraola A. Shokunbi, Ikeoluwa Lagunju, Olugbemiro Sodeinde, Delmiro Fernandez-Reyes:
Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data. CoRR abs/1906.07502 (2019) - [i30]Gaurav Singh, Zahra Sabet, John Shawe-Taylor, James Thomas:
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation. CoRR abs/1910.09255 (2019) - [i29]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. CoRR abs/1911.09471 (2019) - [i28]Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor:
Towards an Integrative Educational Recommender for Lifelong Learners. CoRR abs/1912.01592 (2019) - 2018
- [j98]Viivi Uurtio
A Tutorial on Canonical Correlation Methods. ACM Comput. Surv. 50(6): 95:1-95:33 (2018) - [j97]Huanfa Chen, Tao Cheng
A Balanced Route Design for Min-Max Multiple-Depot Rural Postman Problem (MMMDRPP): a police patrolling case. Int. J. Geogr. Inf. Sci. 32(1): 169-190 (2018) - [j96]Kira Kempinska, Paul A. Longley, John Shawe-Taylor
Interactional regions in cities: making sense of flows across networked systems. Int. J. Geogr. Inf. Sci. 32(7): 1348-1367 (2018) - [c141]Gaurav Singh, James Thomas
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. EMNLP 2018: 2837-2842 - [c140]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization Under Fairness Constraints. NeurIPS 2018: 2796-2806 - [c139]Omar Rivasplata, Csaba Szepesvári, John Shawe-Taylor, Emilio Parrado-Hernández, Shiliang Sun:
PAC-Bayes bounds for stable algorithms with instance-dependent priors. NeurIPS 2018: 9234-9244 - [c138]Fabio S. Ferreira
Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships. PRNI 2018: 1-4 - [i27]Gaurav Singh, James Thomas, John Shawe-Taylor:
Improving Active Learning in Systematic Reviews. CoRR abs/1801.09496 (2018) - [i26]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization under Fairness Constraints. CoRR abs/1802.08626 (2018) - [i25]Tobias Baumann, Thore Graepel, John Shawe-Taylor:
Adaptive Mechanism Design: Learning to Promote Cooperation. CoRR abs/1806.04067 (2018) - [i24]Omar Rivasplata, Emilio Parrado-Hernández, John Shawe-Taylor
PAC-Bayes bounds for stable algorithms with instance-dependent priors. CoRR abs/1806.06827 (2018) - [i23]Seth Nabarro, Tristan Fletcher, John Shawe-Taylor:
Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression. CoRR abs/1806.10873 (2018) - [i22]Gaurav Singh, John Shawe-Taylor:
Faster Convergence & Generalization in DNNs. CoRR abs/1807.11414 (2018) - [i21]Gaurav Singh, James Thomas, Iain James Marshall, John Shawe-Taylor, Byron C. Wallace:
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. CoRR abs/1810.01468 (2018) - [i20]Luke R. Harries, Suyi Zhang, Geoffroy Dubourg-Felonneau, James H. R. Farmery, Jonathan Sinai, Belle Taylor, Nirmesh Patel, John W. Cassidy, John Shawe-Taylor, Harry W. Clifford:
Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection. CoRR abs/1811.11674 (2018) - 2017
- [j95]Shiliang Sun, John Shawe-Taylor
PAC-Bayes analysis of multi-view learning. Inf. Fusion 35: 117-131 (2017) - [j94]Simon Cousins
High-probability minimax probability machines. Mach. Learn. 106(6): 863-886 (2017) - [c137]Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski:
Localized Lasso for High-Dimensional Regression. AISTATS 2017: 325-333 - [c136]Gaurav Singh, Iain James Marshall
A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation. CIKM 2017: 1519-1528 - [i19]Viivi Uurtio, João M. Monteiro, Jaz S. Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, Juho Rousu:
A Tutorial on Canonical Correlation Methods. CoRR abs/1711.02391 (2017) - 2016
- [c135]Guy Lever, John Shawe-Taylor, Ronnie Stafford, Csaba Szepesvári:
Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning. AAAI 2016: 1779-1787 - [c134]Michele Donini
Distributed variance regularized Multitask Learning. IJCNN 2016: 3101-3109 - [c133]Michele Donini
A multimodal multiple kernel learning approach to Alzheimer's disease detection. MLSP 2016: 1-6 - [i18]Diana Borsa, Thore Graepel, John Shawe-Taylor:
Learning Shared Representations in Multi-task Reinforcement Learning. CoRR abs/1603.02041 (2016) - [i17]Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski:
Sparse Network Lasso for Local High-dimensional Regression. CoRR abs/1603.06743 (2016) - [i16]Dorota Glowacka, Yee Whye Teh, John Shawe-Taylor:
Image Retrieval with a Bayesian Model of Relevance Feedback. CoRR abs/1603.09522 (2016) - [i15]Gaurav Singh, Fabrizio Silvestri, John Shawe-Taylor:
Neighborhood Sensitive Mapping for Zero-Shot Classification using Independently Learned Semantic Embeddings. CoRR abs/1605.08242 (2016) - 2015
- [j93]Maria João Duarte Rosa, Liana Catarina Lima Portugal, Tim Hahn
Sparse network-based models for patient classification using fMRI. NeuroImage 105: 493-506 (2015) - [j92]Ian J. Goodfellow
Challenges in representation learning: A report on three machine learning contests. Neural Networks 64: 59-63 (2015) - [c132]Joao M. Monteiro, Anil Rao, John Ashburner, John Shawe-Taylor
Multivariate Effect Ranking via Adaptive Sparse PLS. PRNI 2015: 25-28 - 2014
- [j91]Niclas Thomas, Katharine Best, Mattia Cinelli
Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence. Bioinform. 30(22): 3181-3188 (2014) - [j90]Shiliang Sun, Zakria Hussain, John Shawe-Taylor
Manifold-preserving graph reduction for sparse semi-supervised learning. Neurocomputing 124: 13-21 (2014) - [j89]Emilio Parrado-Hernández
Discovering brain regions relevant to obsessive-compulsive disorder identification through bagging and transduction. Medical Image Anal. 18(3): 435-448 (2014) - [j88]Jane M. Rondina, Tim Hahn
SCoRS - A Method Based on Stability for Feature Selection and Apping in Neuroimaging. IEEE Trans. Medical Imaging 33(1): 85-98 (2014) - [j87]Jane M. Rondina, Tim Hahn
Correction to "SCoRS - A Method Based on Stability for Feature Selection and Mapping in Neuroimaging". IEEE Trans. Medical Imaging 33(3): 794 (2014) - [c131]Sohan Seth, John Shawe-Taylor
Retrieval of Experiments by Efficient Comparison of Marginal Likelihoods. ICONIP (2) 2014: 135-142 - [c130]John Shawe-Taylor:
Deep-er Kernels. ICPRAM 2014: IS-9 - [c129]João M. Monteiro, Anil Rao, John Ashburner, John Shawe-Taylor
Leveraging Clinical Data to Enhance Localization of Brain Atrophy. MLINI@NIPS 2014: 60-68 - [c128]Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor:
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. NIPS 2014: 873-881 - [c127]Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes:
Learning Non-Linear Feature Maps, With An Application To Representation Learning. ICLR (Workshop Poster) 2014 - [i14]Sohan Seth, John Shawe-Taylor, Samuel Kaski:
Retrieval of Experiments by Efficient Estimation of Marginal Likelihood. CoRR abs/1402.4653 (2014) - [i13]Shiliang Sun, John Shawe-Taylor:
PAC-Bayes Analysis of Multi-view Learning. CoRR abs/1406.5614 (2014) - [i12]Zakria Hussain, Arto Klami, Jussi Kujala, Alex Po Leung, Kitsuchart Pasupa
PinView: Implicit Feedback in Content-Based Image Retrieval. CoRR abs/1410.0471 (2014) - 2013
- [j86]Niclas Thomas, James M. Heather
Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine. Bioinform. 29(5): 542-550 (2013) - [j85]Juho Rousu, Daniel D. Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor
Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria. PLoS Comput. Biol. 9(4) (2013) - [j84]Guy Lever, François Laviolette, John Shawe-Taylor
Tighter PAC-Bayes bounds through distribution-dependent priors. Theor. Comput. Sci. 473: 4-28 (2013) - [c126]Kitsuchart Pasupa
Drug screening with Elastic-net multiple kernel learning. BIBE 2013: 1-5 - [c125]Steffen Grünewälder, Arthur Gretton, John Shawe-Taylor:
Smooth Operators. ICML (3) 2013: 1184-1192 - [c124]Ian J. Goodfellow
Challenges in Representation Learning: A Report on Three Machine Learning Contests. ICONIP (3) 2013: 117-124 - [c123]Maria João Duarte Rosa, Liana Catarina Lima Portugal, John Shawe-Taylor
Sparse Network-Based Models for Patient Classification Using fMRI. PRNI 2013: 66-69 - [c122]Jane Maryam Rondina, John Shawe-Taylor
Stability-Based Multivariate Mapping Using SCoRS. PRNI 2013: 198-202 - [i11]Jan Rupnik, Primoz Skraba, John Shawe-Taylor, Sabrina Guettes:
A Comparison of Relaxations of Multiset Cannonical Correlation Analysis and Applications. CoRR abs/1302.0974 (2013) - [i10]Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio:
Challenges in Representation Learning: A report on three machine learning contests. CoRR abs/1307.0414 (2013) - [i9]Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes:
Learning Non-Linear Feature Maps. CoRR abs/1311.5636 (2013) - 2012
- [j83]Martin Sewell, John Shawe-Taylor
Forecasting foreign exchange rates using kernel methods. Expert Syst. Appl. 39(9): 7652-7662 (2012) - [j82]Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor, Shiliang Sun:
PAC-bayes bounds with data dependent priors. J. Mach. Learn. Res. 13: 3507-3531 (2012) - [j81]Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor
PAC-Bayesian Inequalities for Martingales. IEEE Trans. Inf. Theory 58(12): 7086-7093 (2012) - [c121]Emilio Parrado-Hernández
Voxel Selection in MRI through Bagging and Conformal Analysis: Application to Detection of Obsessive Compulsive Disorder. PRNI 2012: 49-52 - [c120]Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer:
PAC-Bayesian Inequalities for Martingales. UAI 2012: 12 - [c119]Dorota Glowacka, Louis Dorard, John Shawe-Taylor:
Preface. ICML On-line Trading of Exploration and Exploitation 2012 - [c118]Yevgeny Seldin, Nicolò Cesa-Bianchi, Peter Auer, François Laviolette, John Shawe-Taylor:
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits. ICML On-line Trading of Exploration and Exploitation 2012: 98-111 - [c117]Guy Lever, Tom Diethe, John Shawe-Taylor:
Data dependent kernels in nearly-linear time. AISTATS 2012: 685-693 - [e11]Dorota Glowacka, Louis Dorard, John Shawe-Taylor:
Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, Bellevue, Washington, USA, July 2, 2011. JMLR Proceedings 26, JMLR.org 2012 [contents] - [i8]Naiyang Guan, Dacheng Tao, Zhigang Luo, John Shawe-Taylor:
MahNMF: Manhattan Non-negative Matrix Factorization. CoRR abs/1207.3438 (2012) - 2011
- [j80]John Shawe-Taylor
A review of optimization methodologies in support vector machines. Neurocomputing 74(17): 3609-3618 (2011) - [j79]Dorota Glowacka, John Shawe-Taylor, Alexander Clark, Colin de la Higuera, Mark Johnson:
Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning. J. Mach. Learn. Res. 12: 1425-1428 (2011) - [j78]David R. Hardoon, John Shawe-Taylor
Sparse canonical correlation analysis. Mach. Learn. 83(3): 331-353 (2011) - [j77]Nicholas Furl
Neural prediction of higher-order auditory sequence statistics. NeuroImage 54(3): 2267-2277 (2011) - [j76]Janaina Mourão Miranda, David R. Hardoon, Tim Hahn
Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine. NeuroImage 58(3): 793-804 (2011) - [j75]Zakria Hussain, John Shawe-Taylor
Design and Generalization Analysis of Orthogonal Matching Pursuit Algorithms. IEEE Trans. Inf. Theory 57(8): 5326-5341 (2011) - [c116]Jane M. Rondina, John Shawe-Taylor
A New Feature Selection Method Based on Stability Theory - Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data. MLINI 2011: 51-59 - [c115]Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, Ronald Ortner:
PAC-Bayesian Analysis of Contextual Bandits. NIPS 2011: 1683-1691 - [c114]Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca:
Preface. WAPA 2011: 1-4 - [c113]Benjamin X. Hall, John Shawe-Taylor, Alan Johnston:
Employing The Complete Face in AVSR to Recover from Facial Occlusions. WAPA 2011: 33-40 - [c112]Zakria Hussain, John Shawe-Taylor:
Improved Loss Bounds For Multiple Kernel Learning. AISTATS 2011: 370-377 - [e10]John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. 2011 [contents] - [e9]Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca:
Proceedings of the Second Workshop on Applications of Pattern Analysis, WAPA 2011, Castro Urdiales, Spain, October 19-21, 2011. JMLR Proceedings 17, JMLR.org 2011 [contents] - [i7]Yevgeny Seldin, François Laviolette, John Shawe-Taylor, Jan Peters, Peter Auer:
PAC-Bayesian Analysis of Martingales and Multiarmed Bandits. CoRR abs/1105.2416 (2011) - [i6]Yevgeny Seldin, Nicolò Cesa-Bianchi, François Laviolette, Peter Auer, John Shawe-Taylor, Jan Peters:
PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off. CoRR abs/1105.4585 (2011) - [i5]Zakria Hussain, John Shawe-Taylor:
A Note on Improved Loss Bounds for Multiple Kernel Learning. CoRR abs/1106.6258 (2011) - [i4]Guy Lever, Tom Diethe, John Shawe-Taylor:
Data-dependent kernels in nearly-linear time. CoRR abs/1110.4416 (2011) - [i3]Yevgeny Seldin, Nicolò Cesa-Bianchi, Peter Auer, François Laviolette, John Shawe-Taylor:
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits. CoRR abs/1110.6755 (2011) - [i2]Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer:
PAC-Bayesian Inequalities for Martingales. CoRR abs/1110.6886 (2011) - 2010
- [b4]Nello Cristianini, John Shawe-Taylor:
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press 2010, ISBN 978-0-521-78019-3, pp. I-XIII, 1-189 - [j74]Shiliang Sun, John Shawe-Taylor:
Sparse Semi-supervised Learning Using Conjugate Functions. J. Mach. Learn. Res. 11: 2423-2455 (2010) - [j73]David R. Hardoon, John Shawe-Taylor
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels. Mach. Learn. 79(1-2): 29-46 (2010) - [j72]Zhuoran Wang, John Shawe-Taylor
A kernel regression framework for SMT. Mach. Transl. 24(2): 87-102 (2010) - [j71]Yuan Shen, Cédric Archambeau, Dan Cornford
A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. J. Signal Process. Syst. 61(1): 51-59 (2010) - [c111]Guy Lever, François Laviolette, John Shawe-Taylor
Distribution-Dependent PAC-Bayes Priors. ALT 2010: 119-133 - [c110]Matthew Higgs, John Shawe-Taylor
A PAC-Bayes Bound for Tailored Density Estimation. ALT 2010: 148-162 - [c109]Zhuoran Wang, John Shawe-Taylor
Semi-supervised feature learning from clinical text. BIBM 2010: 462-466 - [c108]John Shawe-Taylor
Data Dependent Priors in PAC-Bayes Bounds. COMPSTAT 2010: 231-240 - [c107]Zakria Hussain, Kitsuchart Pasupa
Learning relevant eye movement feature spaces across users. ETRA 2010: 181-185 - [c106]John Shawe-Taylor
Multivariate Bandits and Their Applications. Intelligent Information Processing 2010: 3 - [c105]Alexandros Papangelis, Vangelis Metsis, John Shawe-Taylor
Sensor placement and coordination via distributed multi-agent cooperative control. PETRA 2010 - [c104]Tom Diethe
Constructing Nonlinear Discriminants from Multiple Data Views. ECML/PKDD (1) 2010: 328-343 - [c103]Zakria Hussain, Alex Po Leung, Kitsuchart Pasupa
Exploration-Exploitation of Eye Movement Enriched Multiple Feature Spaces for Content-Based Image Retrieval. ECML/PKDD (1) 2010: 554-569 - [c102]Tom Diethe, Nello Cristianini, John Shawe-Taylor:
Preface. WAPA 2010: 1-3 - [c101]Peter Auer, Zakria Hussain, Samuel Kaski, Arto Klami, Jussi Kujala, Jorma Laaksonen, Alex Po Leung, Kitsuchart Pasupa, John Shawe-Taylor:
Pinview: Implicit Feedback in Content-Based Image Retrieval. WAPA 2010: 51-57 - [c100]Dorota Glowacka, John Shawe-Taylor:
Content-based Image Retrieval with Multinomial Relevance Feedback. ACML 2010: 111-125 - [c99]Tristan Fletcher, Zakria Hussain, John Shawe-Taylor:
Multiple Kernel Learning on the Limit Order Book. WAPA 2010: 167-174 - [c98]Steffen Grünewälder, Jean-Yves Audibert, Manfred Opper, John Shawe-Taylor:
Regret Bounds for Gaussian Process Bandit Problems. AISTATS 2010: 273-280 - [p1]Süreyya Özögür-Akyüz, Zakria Hussain, John Shawe-Taylor
Prediction with the SVM Using Test Point Margins. Data Mining 2010: 147-158 - [e8]John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta:
Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2010 [contents] - [e7]Tom Diethe, Nello Cristianini, John Shawe-Taylor:
Proceedings of the First Workshop on Applications of Pattern Analysis, WAPA 2010, Cumberland Lodge, Windsor, UK, September 1-3, 2010. JMLR Proceedings 11, JMLR.org 2010 [contents] - [i1]Louis Dorard, John Shawe-Taylor:
Gaussian Process Bandits for Tree Search. CoRR abs/1009.0605 (2010)
2000 – 2009
- 2009
- [j70]John Shawe-Taylor
Technical perspective - Machine learning for complex predictions. Commun. ACM 52(11): 96 (2009) - [j69]Simon Durrant
GLM and SVM analyses of neural response to tonal and atonal stimuli: new techniques and a comparison. Connect. Sci. 21(2&3): 161-175 (2009) - [j68]Simon Durrant, David R. Hardoon, André Brechmann, John Shawe-Taylor
GLM and SVM analyses of neural response to tonal and atonal stimuli: new techniques and a comparison. Connect. Sci. 21(4): 383 (2009) - [j67]Süreyya Özögür-Akyüz, John Shawe-Taylor
Pattern analysis for the prediction of fungal pro-peptide cleavage sites. Discret. Appl. Math. 157(10): 2388-2394 (2009) - [j66]Aleksander Kolcz, Dunja Mladenic
Guest editors' introduction: special issue of selected papers from ECML PKDD 2009. Data Min. Knowl. Discov. 19(2): 173-175 (2009) - [j65]David R. Hardoon, John Shawe-Taylor
Convergence analysis of kernel Canonical Correlation Analysis: theory and practice. Mach. Learn. 74(1): 23-38 (2009) - [j64]Aleksander Kolcz, Dunja Mladenic
Guest editors' introduction: Special Issue from ECML PKDD 2009. Mach. Learn. 76(2-3): 175-177 (2009) - [j63]Charanpal Dhanjal, Steve R. Gunn, John Shawe-Taylor
Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares. IEEE Trans. Pattern Anal. Mach. Intell. 31(8): 1347-1361 (2009) - [j62]Antti Ajanki, David R. Hardoon, Samuel Kaski, Kai Puolamäki
Can eyes reveal interest? Implicit queries from gaze patterns. User Model. User Adapt. Interact. 19(4): 307-339 (2009) - [c97]Lucia Specia, Nicola Cancedda, Marc Dymetman, Craig Saunders, Marco Turchi, Nello Cristianini, Zhuoran Wang, John Shawe-Taylor:
Sentence-level confidence estimation for MT. SMART@EAMT 2009 - [c96]Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák:
Large-margin structural prediction via linear programming. SMART@EAMT 2009 - [c95]Dorota Glowacka, Louis Dorard, Alan Medlar, John Shawe-Taylor
Prior Knowledge in Learning Finite Parameter Spaces. FG 2009: 199-213 - [c94]Lucia Specia, Marco Turqui, Zhuoran Wang, John Shawe-Taylor, Craig Saunders:
Improving the Confidence of Machine Translation Quality Estimates. MTSummit 2009 - [c93]Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor:
Matching Pursuit Kernel Fisher Discriminant Analysis. AISTATS 2009: 121-128 - [c92]John Shawe-Taylor, David R. Hardoon:
PAC-Bayes Analysis Of Maximum Entropy Classification. AISTATS 2009: 480-487 - [c91]Zhuoran Wang, John Shawe-Taylor:
Large-Margin Structured Prediction via Linear Programming. AISTATS 2009: 599-606 - [e6]Wray L. Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I. Lecture Notes in Computer Science 5781, Springer 2009, ISBN 978-3-642-04179-2 [contents] - [e5]Wray L. Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II. Lecture Notes in Computer Science 5782, Springer 2009, ISBN 978-3-642-04173-0 [contents] - 2008
- [j61]Craig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer:
Using string kernels to identify famous performers from their playing style. Intell. Data Anal. 12(4): 425-440 (2008) - [j60]Marco Gillies, Xueni Pan, Mel Slater
Responsive listening behavior. Comput. Animat. Virtual Worlds 19(5): 579-589 (2008) - [c90]Zakria Hussain, John Shawe-Taylor:
Theory of matching pursuit. NIPS 2008: 721-728 - [c89]Zhuoran Wang, John Shawe-Taylor:
Kernel Regression Framework for Machine Translation: UCL System Description for WMT 2008 Shared Translation Task. WMT@ACL 2008: 155-158 - 2007
- [j59]Alexander N. Dolia, Christopher J. Harris
Kernel ellipsoidal trimming. Comput. Stat. Data Anal. 52(1): 309-324 (2007) - [j58]Sándor Szedmák, John Shawe-Taylor
Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing 70(7-9): 1254-1264 (2007) - [j57]Yaoyong Li, John Shawe-Taylor
Advanced learning algorithms for cross-language patent retrieval and classification. Inf. Process. Manag. 43(5): 1183-1199 (2007) - [j56]Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, S. Charles Brubaker, Matthew D. Mullin:
Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data. J. Mach. Learn. Res. 8: 2533-2549 (2007) - [j55]David R. Hardoon, Janaina Mourão Miranda, Michael J. Brammer, John Shawe-Taylor
Unsupervised analysis of fMRI data using kernel canonical correlation. NeuroImage 37(4): 1250-1259 (2007) - [j54]Amiran Ambroladze, Emilio Parrado-Hernández
Complexity of pattern classes and the Lipschitz property. Theor. Comput. Sci. 382(3): 232-246 (2007) - [c88]Petroula Tsampouka, John Shawe-Taylor
Approximate maximum margin algorithms with rules controlled by the number of mistakes. ICML 2007: 903-910 - [c87]Zakria Hussain, John Shawe-Taylor
Using Generalization Error Bounds to Train the Set Covering Machine. ICONIP (1) 2007: 258-268 - [c86]David R. Hardoon, Janaina Mourão Miranda, Michael J. Brammer, John Shawe-Taylor
Using Image Stimuli to Drive fMRI Analysis. ICONIP (1) 2007: 477-486 - [c85]Halis Altun
New feature selection frameworks in emotion recognition to evaluate the informative power of speech related features. ISSPA 2007: 1-4 - [c84]Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák:
Kernel Regression Based Machine Translation. HLT-NAACL (Short Papers) 2007: 185-188 - [c83]Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor:
Variational Inference for Diffusion Processes. NIPS 2007: 17-24 - [c82]Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor:
Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice 2007: 1-16 - [c81]David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, Samuel Kaski:
Information Retrieval by Inferring Implicit Queries from Eye Movements. AISTATS 2007: 179-186 - [c80]Kristiaan Pelckmans, John Shawe-Taylor, Johan A. K. Suykens, Bart De Moor:
Margin based Transductive Graph Cuts using Linear Programming. AISTATS 2007: 363-370 - [c79]John Shawe-Taylor, Alexander N. Dolia:
A Framework for Probability Density Estimation. AISTATS 2007: 468-475 - [e4]Michael R. Berthold, John Shawe-Taylor, Nada Lavrac:
Advances in Intelligent Data Analysis VII, 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings. Lecture Notes in Computer Science 4723, Springer 2007, ISBN 978-3-540-74824-3 [contents] - 2006
- [j53]Yaoyong Li, John Shawe-Taylor
Using KCCA for Japanese-English cross-language information retrieval and document classification. J. Intell. Inf. Syst. 27(2): 117-133 (2006) - [j52]Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor:
Kernel-Based Learning of Hierarchical Multilabel Classification Models. J. Mach. Learn. Res. 7: 1601-1626 (2006) - [c78]David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
A Correlation Approach for Automatic Image Annotation. ADMA 2006: 681-692 - [c77]Petroula Tsampouka, John Shawe-Taylor
Constant Rate Approximate Maximum Margin Algorithms. ECML 2006: 437-448 - [c76]Alexander N. Dolia, Tijl De Bie, Christopher J. Harris
The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces. ECML 2006: 630-637 - [c75]Sándor Szedmák, John Shawe-Taylor:
Synthesis of maximum margin and multiview learning using unlabeled data. ESANN 2006: 479-484 - [c74]Alain D. Lehmann, John Shawe-Taylor
A probabilistic model for text kernels. ICML 2006: 537-544 - [c73]Amiran Ambroladze, Emilio Parrado-Hernández, John Shawe-Taylor:
Tighter PAC-Bayes Bounds. NIPS 2006: 9-16 - [e3]Craig Saunders, Marko Grobelnik, Steve R. Gunn, John Shawe-Taylor:
Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers. Lecture Notes in Computer Science 3940, Springer 2006, ISBN 3-540-34137-4 [contents] - 2005
- [j51]Juho Rousu, John Shawe-Taylor:
Efficient Computation of Gapped Substring Kernels on Large Alphabets. J. Mach. Learn. Res. 6: 1323-1344 (2005) - [j50]Thore Graepel, Ralf Herbrich, John Shawe-Taylor
PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. Mach. Learn. 59(1-2): 55-76 (2005) - [j49]Shutao Li, John Shawe-Taylor
Comparison and fusion of multiresolution features for texture classification. Pattern Recognit. Lett. 26(5): 633-638 (2005) - [j48]John Shawe-Taylor
On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA. IEEE Trans. Inf. Theory 51(7): 2510-2522 (2005) - [c72]Matthew Henderson
Mixture of Vector Experts. ALT 2005: 386-398 - [c71]Petroula Tsampouka, John Shawe-Taylor
Analysis of Generic Perceptron-Like Large Margin Classifiers. ECML 2005: 750-758 - [c70]Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
Learning hierarchical multi-category text classification models. ICML 2005: 744-751 - [c69]Anders Meng, John Shawe-Taylor:
An Investigation of Feature Models for Music Genre Classification Using the Support Vector Classifier. ISMIR 2005: 604-609 - [c68]Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen
The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176 - [c67]Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák:
Two view learning: SVM-2K, Theory and Practice. NIPS 2005: 355-362 - 2004
- [b3]John Shawe-Taylor, Nello Cristianini:
Kernel Methods for Pattern Analysis. Cambridge University Press 2004, ISBN 9780511809682 - [b2]John Shawe-Taylor, Nello Cristianini:
Kernel Methods for Pattern Analysis. Cambridge University Press 2004, ISBN 978-0-521-81397-6, pp. I-XIV, 1-462 - [j47]David R. Hardoon, Sándor Szedmák, John Shawe-Taylor
Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Comput. 16(12): 2639-2664 (2004) - [c66]Amiran Ambroladze, John Shawe-Taylor
Complexity of Pattern Classes and Lipschitz Property. ALT 2004: 181-193 - [c65]Hongying Meng
Support Vector Machine to Synthesise Kernels. Deterministic and Statistical Methods in Machine Learning 2004: 242-255 - [c64]Craig Saunders, David R. Hardoon, John Shawe-Taylor
Using String Kernels to Identify Famous Performers from Their Playing Style. ECML 2004: 384-395 - [c63]Shutao Li, John Shawe-Taylor
Texture Classification by Combining Wavelet and Contourlet Features. SSPR/SPR 2004: 1126-1134 - [e2]John Shawe-Taylor, Yoram Singer:
Learning Theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings. Lecture Notes in Computer Science 3120, Springer 2004, ISBN 3-540-22282-0 [contents] - 2003
- [c62]Jaz S. Kandola, John Shawe-Taylor:
Refining Kernels for Regression and Uneven Classification Problems. AISTATS 2003: 157-162 - [c61]Jaz S. Kandola, Thore Graepel, John Shawe-Taylor
Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming. COLT 2003: 288-302 - [c60]Amiran Ambroladze, John Shawe-Taylor
When Is Small Beautiful? COLT 2003: 729-730 - [c59]Jure Leskovec, John Shawe-Taylor:
Linear Programming Boosting for Uneven Datasets. ICML 2003: 456-463 - [c58]Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova:
The Set Covering Machine with Data-Dependent Half-Spaces. ICML 2003: 520-527 - [c57]Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor:
Semi-Definite Programming by Perceptron Learning. NIPS 2003: 457-464 - [c56]Yaoyong Li, John Shawe-Taylor:
The SVM With Uneven Margins and Chinese Document Categorization. PACLIC 2003: 216-227 - 2002
- [j46]Huma Lodhi, Grigoris I. Karakoulas, John Shawe-Taylor:
Boosting strategy for classification. Intell. Data Anal. 6(2): 149-174 (2002) - [j45]Nello Cristianini, John Shawe-Taylor
Latent Semantic Kernels. J. Intell. Inf. Syst. 18(2-3): 127-152 (2002) - [j44]Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins:
Text Classification using String Kernels. J. Mach. Learn. Res. 2: 419-444 (2002) - [j43]Mario Marchand, John Shawe-Taylor:
The Set Covering Machine. J. Mach. Learn. Res. 3: 723-746 (2002) - [j42]Ayhan Demiriz, Kristin P. Bennett, John Shawe-Taylor
Linear Programming Boosting via Column Generation. Mach. Learn. 46(1-3): 225-254 (2002) - [j41]Yves Van de Peer, John Shawe-Taylor, Jayabalan Joseph, Axel Meyer
Wanda: a database of duplicated fish genes. Nucleic Acids Res. 30(1): 109-112 (2002) - [j40]Ying Guo, Peter L. Bartlett
Covering numbers for support vector machines. IEEE Trans. Inf. Theory 48(1): 239-250 (2002) - [j39]John Shawe-Taylor
On the generalization of soft margin algorithms. IEEE Trans. Inf. Theory 48(10): 2721-2735 (2002) - [c55]John Shawe-Taylor
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. ALT 2002: 23-40 - [c54]John Shawe-Taylor
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. Discovery Science 2002: 12 - [c53]Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola:
The Perceptron Algorithm with Uneven Margins. ICML 2002: 379-386 - [c52]Craig Saunders, Hauke Tschach, John Shawe-Taylor:
Syllables and other String Kernel Extensions. ICML 2002: 530-537 - [c51]John Shawe-Taylor, Christopher K. I. Williams:
The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. NIPS 2002: 367-374 - [c50]John Langford, John Shawe-Taylor:
PAC-Bayes & Margins. NIPS 2002: 423-430 - [c49]Craig Saunders, John Shawe-Taylor, Alexei Vinokourov:
String Kernels, Fisher Kernels and Finite State Automata. NIPS 2002: 633-640 - [c48]Jaz S. Kandola, John Shawe-Taylor, Nello Cristianini:
Learning Semantic Similarity. NIPS 2002: 657-664 - [c47]Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor:
The Decision List Machine. NIPS 2002: 921-928 - [c46]Alexei Vinokourov, John Shawe-Taylor, Nello Cristianini:
Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis. NIPS 2002: 1473-1480 - [c45]Nicola Cancedda, Cyril Goutte, Jean-Michel Renders, Nicolò Cesa-Bianchi, Alex Conconi, Yaoyong Li, John Shawe-Taylor, Alexei Vinokourov, Thore Graepel, Claudio Gentile:
Kernel Methods for Document Filtering. TREC 2002 - 2001
- [j38]John Shawe-Taylor:
Neural Network Learning: Theoretical Foundation. AI Mag. 22(2): 99-100 (2001) - [j37]Peter Burge, John Shawe-Taylor
An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection. J. Parallel Distributed Comput. 61(7): 915-925 (2001) - [j36]Bernhard Schölkopf, John C. Platt, John Shawe-Taylor
Estimating the Support of a High-Dimensional Distribution. Neural Comput. 13(7): 1443-1471 (2001) - [c44]Nello Cristianini, John Shawe-Taylor, Huma Lodhi:
Latent Semantic Kernels. ICML 2001: 66-73 - [c43]Thorsten Joachims, Nello Cristianini, John Shawe-Taylor:
Composite Kernels for Hypertext Categorisation. ICML 2001: 250-257 - [c42]Mario Marchand, John Shawe-Taylor:
Learning with the Set Covering Machine. ICML 2001: 345-352 - [c41]Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola:
On Kernel-Target Alignment. NIPS 2001: 367-373 - [c40]John Shawe-Taylor, Nello Cristianini, Jaz S. Kandola:
On the Concentration of Spectral Properties. NIPS 2001: 511-517 - [c39]Nello Cristianini, John Shawe-Taylor, Jaz S. Kandola:
Spectral Kernel Methods for Clustering. NIPS 2001: 649-655 - 2000
- [b1]Nello Cristianini, John Shawe-Taylor:
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press 2000, ISBN 9780511801389 - [j35]Tomaz Pisanski, John Shawe-Taylor
Characterizing Graph Drawing with Eigenvectors. J. Chem. Inf. Comput. Sci. 40(3): 567-571 (2000) - [j34]Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor
Enlarging the Margins in Perceptron Decision Trees. Mach. Learn. 41(3): 295-313 (2000) - [c38]Thore Graepel, Ralf Herbrich, John Shawe-Taylor:
Generalisation Error Bounds for Sparse Linear Classifiers. COLT 2000: 298-303 - [c37]Ralf Herbrich, Thore Graepel, John Shawe-Taylor:
Sparsity vs. Large Margins for Linear Classifiers. COLT 2000: 304-308 - [c36]Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor:
A Column Generation Algorithm For Boosting. ICML 2000: 65-72 - [c35]Matthias Rychetsky, John Shawe-Taylor, Manfred Glesner:
Direct Bayes Point Machines. ICML 2000: 815-822 - [c34]Huma Lodhi, Grigoris I. Karakoulas, John Shawe-Taylor
Boosting the Margin Distribution. IDEAL 2000: 54-59 - [c33]Huma Lodhi, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins:
Text Classification using String Kernels. NIPS 2000: 563-569 - [c32]Barry Rising, John Shawe-Taylor
Graph Colouring by Maximal Evidence Edge Adding. PATAT 2000: 294-308
1990 – 1999
- 1999
- [j33]John Shawe-Taylor, Keith Howker, Peter Burge:
Detection of fraud in mobile telecommunications. Inf. Secur. Tech. Rep. 4(1): 3-15 (1999) - [j32]John Shawe-Taylor, Keith Howker, Peter Burge:
Detection of fraud in mobile telecommunications. Inf. Secur. Tech. Rep. 4(1): 16-28 (1999) - [j31]John Shawe-Taylor
Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT'97. Mach. Learn. 35(3): 191-192 (1999) - [c31]Ying Guo, Peter L. Bartlett, John Shawe-Taylor
Covering Numbers for Support Vector Machines. COLT 1999: 267-277 - [c30]John Shawe-Taylor
Further Results on the Margin Distribution. COLT 1999: 278-285 - [c29]Nello Cristianini, Colin Campbell, John Shawe-Taylor:
A multiplicative updating algorithm for training support vector machine. ESANN 1999: 189-194 - [c28]John Shawe-Taylor
Margin Distribution Bounds on Generalization. EuroCOLT 1999: 263-273 - [c27]John Shawe-Taylor
Generalization Performance of Classifiers in Terms of Observed Covering Numbers. EuroCOLT 1999: 274-284 - [c26]Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor:
Large Margin Trees for Induction and Transduction. ICML 1999: 474-483 - [c25]Alexander J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson:
The Entropy Regularization Information Criterion. NIPS 1999: 342-348 - [c24]John C. Platt, Nello Cristianini, John Shawe-Taylor:
Large Margin DAGs for Multiclass Classification. NIPS 1999: 547-553 - [c23]Bernhard Schölkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor, John C. Platt:
Support Vector Method for Novelty Detection. NIPS 1999: 582-588 - 1998
- [j30]John Shawe-Taylor
Classification Accuracy Based on Observed Margin. Algorithmica 22(1/2): 157-172 (1998) - [j29]John Shawe-Taylor
Special Issue of DAM on the Vapnik-chervonenkis Dimension. Discret. Appl. Math. 86(1): 1-2 (1998) - [j28]John Shawe-Taylor
Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Trans. Inf. Theory 44(5): 1926-1940 (1998) - [c22]Nello Cristianini, John Shawe-Taylor, Peter Sykacek:
Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space. ICML 1998: 109-117 - [c21]Barry Rising, Max van Daalen, John Shawe-Taylor, Peter Burge, Janez Zerovnik:
A Neural Accelerator for Graph Colouring Based on an Edge Adding Technique. NC 1998: 652-656 - [c20]Nello Cristianini, Colin Campbell, John Shawe-Taylor:
Dynamically Adapting Kernels in Support Vector Machines. NIPS 1998: 204-210 - [c19]Grigoris I. Karakoulas, John Shawe-Taylor:
Optimizing Classifers for Imbalanced Training Sets. NIPS 1998: 253-259 - 1997
- [j27]Martin Anthony
A Sufficient Condition for Polynomial Distribution-dependent Learnability. Discret. Appl. Math. 77(1): 1-12 (1997) - [c18]John Shawe-Taylor, Robert C. Williamson:
A PAC Analysis of a Bayesian Estimator. COLT 1997: 2-9 - [c17]John Shawe-Taylor
Confidence Estimates of Classification Accuracy on New Examples. EuroCOLT 1997: 260-271 - [c16]Barry Rising, Max van Daalen, Peter Burge, John Shawe-Taylor
Parallel Graph colouring using FPGAs. FPL 1997: 121-130 - [c15]John Shawe-Taylor, Nello Cristianini:
Data-Dependent Structural Risk Minimization for Perceptron Decision Trees. NIPS 1997: 336-342 - 1996
- [j26]Martin Anthony
Valid Generalisation from Approximate Interpolation. Comb. Probab. Comput. 5: 191-214 (1996) - [j25]John Shawe-Taylor
Fast String Matching in Stationary Ergodic Sources. Comb. Probab. Comput. 5: 415-427 (1996) - [j24]Jeffrey Wood, John Shawe-Taylor
Representation Theory and Invariant Neural Networks. Discret. Appl. Math. 69(1-2): 33-60 (1996) - [j23]Jieyu Zhao, John Shawe-Taylor
Learning in Stochastic Bit Stream Neural Networks. Neural Networks 9(6): 991-998 (1996) - [j22]Jeffrey Wood, John Shawe-Taylor
A unifying framework for invariant pattern recognition. Pattern Recognit. Lett. 17(14): 1415-1422 (1996) - [c14]John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony:
A Framework for Structural Risk Minimisation. COLT 1996: 68-76 - [c13]Jonathan Baxter, John Shawe-Taylor
Learning to Compress Ergodic Sources. Data Compression Conference 1996: 423 - [c12]Jieyu Zhao, John Shawe-Taylor:
A recurrent network with stochastic weights. ICNN 1996: 1302-1307 - 1995
- [j21]Martin Anthony
On Specifying Boolean Functions by Labelled Examples. Discret. Appl. Math. 61(1): 1-25 (1995) - [j20]John Shawe-Taylor
Sample Sizes for Threshold Networks with Equivalences. Inf. Comput. 118(1): 65-72 (1995) - [c11]Carlos Domingo, John Shawe-Taylor
The Complexity of Learning Minor Closed Graph Classes. ALT 1995: 249-260 - [c10]John Shawe-Taylor
Sample Sizes for Sigmoidal Neural Networks. COLT 1995: 258-264 - [c9]Jeffrey Wood, John Shawe-Taylor:
Neural networks for invariant pattern recognition. ESANN 1995 - [c8]John Shawe-Taylor:
Generalisation Analysis for Classes of Continuous Neural Networks. ICNN 1995: 2944-2948 - [c7]John Shawe-Taylor, Jieyu Zhao:
Generalisation of A Class of Continuous Neural Networks. NIPS 1995: 267-273 - 1994
- [j19]Martin Anthony
A Result of Vapnik with Applications. Discret. Appl. Math. 52(2): 211 (1994) - [j18]John Shawe-Taylor
Coverings of complete bipartite graphs and associated structures. Discret. Math. 134(1-3): 151-160 (1994) - [j17]John Shawe-Taylor
Homeomorphism of 2-Complexes is Graph Isomorphism Complete. SIAM J. Comput. 23(1): 120-132 (1994) - [j16]Jong Yong Kim, John Shawe-Taylor
Fast String Matching using an n -gram Algorithm. Softw. Pract. Exp. 24(1): 79-88 (1994) - [j15]Peter Jeavons, David A. Cohen, John Shawe-Taylor
Generating binary sequences for stochastic computing. IEEE Trans. Inf. Theory 40(3): 716-720 (1994) - [c6]Patrick W. Fowler, Tomaz Pisanski, John Shawe-Taylor
Molecular Graph Eigenvectors for Molecular Coordinates. GD 1994: 282-285 - [e1]John Shawe-Taylor, Martin Anthony:
Proceedings of the First European Conference on Computational Learning Theory, EuroCOLT 1993, London, UK, December 20-22, 1993. Oxford University Press 1994, ISBN 0-19-853492-2 [contents] - 1993
- [j14]Martin Anthony
Using the Perceptron Algorithm to Find Consistent Hypotheses. Comb. Probab. Comput. 2: 385-387 (1993) - [j13]John Shawe-Taylor
Bounding Sample Size with the Vapnik-Chervonenkis Dimension. Discret. Appl. Math. 42(1): 65-73 (1993) - [j12]Martin Anthony
A Result of Vapnik with Applications. Discret. Appl. Math. 47(3): 207-217 (1993) - [j11]John Shawe-Taylor
Symmetries and discriminability in feedforward network architectures. IEEE Trans. Neural Networks 4(5): 816-826 (1993) - [c5]Martin Anthony, John Shawe-Taylor:
Valid generalisation of functions from close approximations on a sample. EuroCOLT 1993: 95-108 - 1992
- [j10]John Shawe-Taylor
Classes of feedforward neural networks and their circuit complexity. Neural Networks 5(6): 971-977 (1992) - [j9]Jong Yong Kim, John Shawe-Taylor
An Approximate String-Matching Algorithm. Theor. Comput. Sci. 92(1): 107-117 (1992) - [c4]Martin Anthony
On Exact Specification by Examples. COLT 1992: 311-318 - [c3]Jong Yong Kim, John Shawe-Taylor
Fast Multiple Keyword Searching. CPM 1992: 41-51 - 1991
- [c2]John Shawe-Taylor:
Threshold Network Learning in the Presence of Equivalences. NIPS 1991: 879-886 - 1990
- [j8]John Shawe-Taylor
Linear programming algorithm for neural networks. Neural Networks 3(5): 575-582 (1990) - [c1]Martin Anthony, Norman Biggs, John Shawe-Taylor:
The Learnability of Formal Concepts. COLT 1990: 246-257
1980 – 1989
- 1988
- [j7]David A. Cohen, C. Mannion, John Shawe-Taylor
Transformational theory of feedforward neural networks. Neural Networks 1(Supplement-1): 83-84 (1988) - 1987
- [j6]John Shawe-Taylor
Automorphism Groups of Primitive Distance-Bitransitive Graphs are Almost Simple. Eur. J. Comb. 8(2): 187-197 (1987) - [j5]Chris D. Godsil
Distance-regularised graphs are distance-regular or distance-biregular. J. Comb. Theory B 43(1): 14-24 (1987) - [j4]John Shawe-Taylor
Information and its Relation to Formalisms for the Complexities of the Real World. J. Inf. Technol. 2(3): 151-155 (1987) - 1985
- [j3]Bojan Mohar, John Shawe-Taylor
Distance-biregular graphs with 2-valent vertices and distance-regular line graphs. J. Comb. Theory B 38(3): 193-203 (1985) - 1983
- [j2]Tomaz Pisanski, John Shawe-Taylor
Edge-colorability of graph bundles. J. Comb. Theory B 35(1): 12-19 (1983) - 1981
- [j1]Tomaz Pisanski, John Shawe-Taylor
Search for minimal trivalent cycle permutation graphs with girth nine. Discret. Math. 36(1): 113-115 (1981)
Coauthor Index
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