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Paulo J. G. Lisboa
Paulo Lisboa
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- affiliation: Liverpool John Moores University, Liverpool, UK
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2020 – today
- 2023
- [j59]Bradley Walters, Sandra Ortega-Martorell
, Iván Olier
How to Open a Black Box Classifier for Tabular Data. Algorithms 16(4): 181 (2023) - [j58]Paulo J. G. Lisboa, Sascha Saralajew, Alfredo Vellido, Ricardo Fernández-Domenech, Thomas Villmann:
The coming of age of interpretable and explainable machine learning models. Neurocomputing 535: 25-39 (2023) - 2022
- [j57]Serkan Aras
Explainable inflation forecasts by machine learning models. Expert Syst. Appl. 207: 117982 (2022) - [c83]Bradley Walters, Sandra Ortega-Martorell
Towards interpretable machine learning for clinical decision support. IJCNN 2022: 1-8 - 2021
- [j56]Raúl V. Casaña Eslava
Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering. Neural Comput. Appl. 33(13): 7521-7539 (2021) - [j55]Paul Fergus
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes. IEEE Trans. Emerg. Top. Comput. Intell. 5(6): 882-892 (2021) - [c82]Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann:
The Coming of Age of Interpretable and Explainable Machine Learning Models. ESANN 2021 - [c81]Bradley Walters, Sandra Ortega-Martorell
The partial response SVM. ESANN 2021 - 2020
- [j54]Raúl V. Casaña Eslava, Paulo J. G. Lisboa, Sandra Ortega-Martorell
Probabilistic quantum clustering. Knowl. Based Syst. 194: 105567 (2020) - [j53]Paul Fergus
Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women. IEEE ACM Trans. Comput. Biol. Bioinform. 17(2): 668-678 (2020) - [j52]Jade Hind, Paulo Lisboa
A Novel Approach to Detecting Epistasis using Random Sampling Regularisation. IEEE ACM Trans. Comput. Biol. Bioinform. 17(5): 1535-1545 (2020) - [c80]Paulo J. G. Lisboa, Sandra Ortega-Martorell
Explaining the Neural Network: A Case Study to Model the Incidence of Cervical Cancer. IPMU (1) 2020: 585-598 - [c79]Paulo J. G. Lisboa, Sandra Ortega-Martorell
Efficient Estimation of General Additive Neural Networks: A Case Study for CTG Data. PKDD/ECML Workshops 2020: 432-446
2010 – 2019
- 2019
- [c78]Daniela Montilla-Trochez, Rodrigo Salas, Alejandro Bertin, Inga Griskova-Bulanova, Paulo Lisboa, Carolina Saavedra:
Convolutional neural network for cognitive task prediction from EEG's auditory steady state responses. CRoNe 2019: 44-50 - [c77]Davide Bacciu, Battista Biggio, Paulo Lisboa, José D. Martín, Luca Oneto, Alfredo Vellido:
Societal Issues in Machine Learning: When Learning from Data is Not Enough. ESANN 2019 - [c76]Philippa Grace McCabe
Comparative Analysis for Computer-Based Decision Support: Case Study of Knee Osteoarthritis. IDEAL (2) 2019: 114-122 - [c75]Raúl V. Casaña Eslava, José David Martín-Guerrero, Sandra Ortega-Martorell
Scalable implementation of measuring distances in a Riemannian manifold based on the Fisher Information metric. IJCNN 2019: 1-7 - [c74]Patrick Riley
A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI. WSOM+ 2019: 294-303 - [c73]Meenal Srivastava, Iván Olier
Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer. WSOM+ 2019: 304-313 - [i5]Raúl V. Casaña Eslava, Paulo J. G. Lisboa, Sandra Ortega-Martorell, Ian H. Jarman, José David Martín-Guerrero:
A Probabilistic framework for Quantum Clustering. CoRR abs/1902.05578 (2019) - [i4]Paul Fergus, Carl Chalmers, Casimiro Aday Curbelo Montañez, Denis Reilly, Paulo Lisboa, Beth Pineles:
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes. CoRR abs/1908.02338 (2019) - [i3]Paulo J. G. Lisboa, Sandra Ortega-Martorell, Sadie Cashman, Iván Olier:
The Partial Response Network. CoRR abs/1908.05978 (2019) - 2018
- [c72]Basma Abdulaimma, Abir Hussain, Paul Fergus, Dhiya Al-Jumeily, Paulo Lisboa, De-Shuang Huang, Naeem Radi:
Improving Type 2 Diabetes Phenotypic Classification by Combining Genetics and Conventional Risk Factors. CEC 2018: 1-7 - [c71]Mohammed Khalaf
A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction. CEC 2018: 1-8 - [c70]Natasa K. Orphanidou, Abir Hussain, Robert Keight, Paulo Lisboa, Jade Hind, Haya Al-Askar:
Predicting Freezing of Gait in Parkinsons Disease Patients Using Machine Learning. CEC 2018: 1-8 - [c69]Jade Hind, Abir Hussain, Dhiya Al-Jumeily, Casimiro Aday Curbelo Montañez
Robust Interpretation of Genomic Data in Chronic Obstructive Pulmonary Disease (COPD). DeSE 2018: 12-17 - [c68]Davide Bacciu, Paulo Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido:
Bioinformatics and medicine in the era of deep learning. ESANN 2018 - [c67]Chelsea Dobbins
A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices. PerCom Workshops 2018: 306-311 - [i2]Paul Fergus, Casimiro Aday Curbelo Montañez, Basma Abdulaimma, Paulo Lisboa, Carl Chalmers:
Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women. CoRR abs/1801.02977 (2018) - [i1]Davide Bacciu, Paulo J. G. Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido:
Bioinformatics and Medicine in the Era of Deep Learning. CoRR abs/1802.09791 (2018) - 2017
- [j51]Raúl V. Casaña Eslava, Ian H. Jarman, Paulo J. G. Lisboa, José David Martín-Guerrero
Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters. Neurocomputing 268: 127-141 (2017) - [c66]Jade Hind, Abir Jaafar Hussain, Dhiya Al-Jumeily
A robust method for the interpretation of genomic data. IJCNN 2017: 3385-3390 - 2016
- [c65]Raúl V. Casaña Eslava, José David Martín-Guerrero, Ian H. Jarman, Paulo J. G. Lisboa:
Performance assessment of quantum clustering in non-spherical data distributions. ESANN 2016 - [c64]José David Martín-Guerrero, Paulo J. G. Lisboa, Alfredo Vellido:
Physics and Machine Learning: Emerging Paradigms. ESANN 2016 - 2015
- [j50]Victor Mocioiu, Sandra Ortega-Martorell
From raw data to data-analysis for magnetic resonance spectroscopy - the missing link: jMRUI2XML. BMC Bioinform. 16: 378:1-378:11 (2015) - [j49]Paulo J. G. Lisboa, José David Martín-Guerrero
Making nonlinear manifold learning models interpretable: The manifold grand tour. Expert Syst. Appl. 42(22): 8982-8988 (2015) - [j48]Maryam Farhadian
Wavelet-based gene selection method for survival prediction in diffuse large B-cell lymphomas patients. Int. J. Data Min. Bioinform. 13(2): 197-210 (2015) - [j47]Abir Jaafar Hussain, Dhiya Al-Jumeily
Hybrid Neural Network Predictive-Wavelet Image Compression System. Neurocomputing 151: 975-984 (2015) - [c63]Héctor Ruiz, Paulo Lisboa, Paul Neilson, Warren Gregson:
Measuring scoring efficiency through goal expectancy estimation. ESANN 2015 - [p3]Davide Bacciu, Paulo J. G. Lisboa, Alessandro Sperduti, Thomas Villmann:
Probabilistic Modeling in Machine Learning. Handbook of Computational Intelligence 2015: 545-575 - 2014
- [j46]Vanya Van Belle, Paulo Lisboa:
White box radial basis function classifiers with component selection for clinical prediction models. Artif. Intell. Medicine 60(1): 53-64 (2014) - [j45]Mark John Taylor, Rod Stables, Bashir Matata, Paulo J. G. Lisboa, Andy Laws
Website design: Technical, social and medical issues for self-reporting by elderly patients. Health Informatics J. 20(2): 136-150 (2014) - [c62]Daniel Urda
Use of q-values to Improve a Genetic Algorithm to Identify Robust Gene Signatures. CIBB 2014: 199-206 - [c61]Sandra Ortega-Martorell
Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy. CIDM 2014: 93-98 - [c60]Sandra Ortega-Martorell
Automatic relevance source determination in human brain tumors using Bayesian NMF. CIDM 2014: 99-104 - [c59]Simon J. Chambers, Ian H. Jarman, Paulo J. G. Lisboa:
A framework for initialising a dynamic clustering algorithm: ART2-A. CIDM 2014: 273-280 - 2013
- [j44]Riccardo Rizzo
Introduction. BMC Bioinform. 14(S-1): I1 (2013) - [j43]Paulo J. G. Lisboa, Terence A. Etchells, Ian H. Jarman, Simon J. Chambers:
Finding reproducible cluster partitions for the k-means algorithm. BMC Bioinform. 14(S-1): S8 (2013) - [j42]Davide Bacciu
Efficient identification of independence networks using mutual information. Comput. Stat. 28(2): 621-646 (2013) - [j41]Héctor Ruiz, Terence A. Etchells, Ian H. Jarman, José David Martín-Guerrero
A principled approach to network-based classification and data representation. Neurocomputing 112: 79-91 (2013) - [j40]Albert Vilamala, Paulo J. G. Lisboa, Sandra Ortega-Martorell
Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours. Pattern Recognit. Lett. 34(14): 1734-1747 (2013) - [c58]Vanya Van Belle, Paulo Lisboa:
Automated selection of interaction effects in sparse kernel methods to predict pregnancy viability. CIDM 2013: 26-31 - [c57]Vanya Van Belle, Paulo Lisboa:
Research directions in interpretable machine learning models. ESANN 2013 - [c56]Paulo J. G. Lisboa:
Interpretability in Machine Learning - Principles and Practice. WILF 2013: 15-21 - 2012
- [j39]Sandra Ortega-Martorell
Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours. BMC Bioinform. 13: 38 (2012) - [j38]Mark Taylor, Emma Higgins
Testing geographical information systems: a case study in a fire prevention support system. J. Syst. Inf. Technol. 14(3): 184-199 (2012) - [j37]Enrique Romero, Tingting Mu, Paulo J. G. Lisboa:
Cohort-based kernel visualisation with scatter matrices. Pattern Recognit. 45(4): 1436-1454 (2012) - [c55]Corneliu T. C. Arsene, Paulo J. G. Lisboa:
Multicentre study design in survival analysis. CIBCB 2012: 135-143 - [c54]Héctor Ruiz, Sandra Ortega-Martorell, Ian H. Jarman, José David Martín-Guerrero, Paulo J. G. Lisboa:
Constructing similarity networks using the Fisher information metric. ESANN 2012 - [c53]Alfredo Vellido, José David Martín-Guerrero, Paulo J. G. Lisboa:
Making machine learning models interpretable. ESANN 2012 - [c52]Corneliu T. C. Arsene, Paulo J. G. Lisboa:
Bayesian Neural Network with and without compensation for competing risks. IJCNN 2012: 1-8 - [c51]Héctor Ruiz, Ian H. Jarman, José David Martín-Guerrero, Sandra Ortega-Martorell
Towards interpretable classifiers with blind signal separation. IJCNN 2012: 1-7 - [c50]Corneliu T. C. Arsene, Paulo J. G. Lisboa:
Bayesian Neural Network Applied in Medical Survival Analysis of Primary Biliary Cirrhosis. UKSim 2012: 81-85 - 2011
- [j36]Malcolm J. Taylor, Bashir Matata, Rod Stables, Andy Laws
Issues in online patient self-reporting of health status. Health Informatics J. 17(1): 5-14 (2011) - [j35]Ian H. Jarman, Terence A. Etchells, Davide Bacciu
Clustering of protein expression data: a benchmark of statistical and neural approaches. Soft Comput. 15(8): 1459-1469 (2011) - [c49]Paulo J. G. Lisboa, Ian H. Jarman, Terence A. Etchells, Simon J. Chambers, Davide Bacciu
Discovering Hidden Pathways in Bioinformatics. CIBB 2011: 49-60 - [c48]Ian H. Jarman, Terence A. Etchells, Paulo J. G. Lisboa, C. M. Beynon, José David Martín-Guerrero:
Clustering categorical data: A stability analysis framework. CIDM 2011: 58-65 - [c47]Ian H. Jarman, Terence A. Etchells, C. Perkins, M. A. Bellis, Paulo J. G. Lisboa:
Scenario Analysis for Local Area Life Expectancy Using Conditional Independence Maps. DeSE 2011: 92-97 - [c46]Héctor Ruiz, Ian H. Jarman, José David Martín-Guerrero, Paulo J. G. Lisboa:
The role of Fisher information in primary data space for neighbourhood mapping. ESANN 2011 - [c45]Alfredo Vellido, José David Martín-Guerrero, Fabrice Rossi, Paulo J. G. Lisboa:
Seeing is believing: The importance of visualization in real-world machine learning applications. ESANN 2011 - [c44]Sandra Ortega-Martorell
Brain Tumor Pathological Area Delimitation through Non-negative Matrix Factorization. ICDM Workshops 2011: 1058-1063 - [c43]Corneliu T. C. Arsene, Paulo J. G. Lisboa:
PLANN-CR-ARD model predictions and Non-parametric estimates with Confidence Intervals. IJCNN 2011: 1553-1561 - [c42]Sandra Ortega-Martorell
Spectral decomposition methods for the analysis of MRS information from human brain tumors. IJCNN 2011: 3279-3284 - [c41]Corneliu T. C. Arsene, Paulo J. G. Lisboa, Elia Biganzoli
Model Selection with PLANN-CR-ARD. IWANN (2) 2011: 210-219 - [e2]Riccardo Rizzo, Paulo J. G. Lisboa:
Computational Intelligence Methods for Bioinformatics and Biostatistics - 7th International Meeting, CIBB 2010, Palermo, Italy, September 16-18, 2010, Revised Selected Papers. Lecture Notes in Computer Science 6685, Springer 2011, ISBN 978-3-642-21945-0 [contents] - 2010
- [j34]Valérie Bourdès, Stéphane Bonnevay, Paulo J. G. Lisboa, Rémy Defrance, David Pérol, Sylvie Chabaud, Thomas Bachelot, Thérèse Gargi, Sylvie Négrier:
Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes. Adv. Artif. Neural Syst. 2010: 309841:1-309841:11 (2010) - [j33]Daniele Soria, Jonathan M. Garibaldi
A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Comput. Biol. Medicine 40(3): 318-330 (2010) - [j32]Paulo J. G. Lisboa, Alfredo Vellido
Data Mining in Cancer Research [Application Notes]. IEEE Comput. Intell. Mag. 5(1): 14-18 (2010) - [c40]Paulo J. G. Lisboa, Alfredo Vellido, José David Martín-Guerrero:
Computational Intelligence in biomedicine: Some contributions. ESANN 2010 - [c39]Ana S. Fernandes, Pedro Alves, Ian H. Jarman, Terence A. Etchells, José Manuel Fonseca, Paulo J. G. Lisboa:
A Clinical Decision Support System for Breast Cancer Patients. DoCEIS 2010: 122-129 - [c38]Ilaria Ardoino
Flexible parametric modelling of the hazard function in breast cancer studies. IJCNN 2010: 1-5 - [c37]Paulo J. G. Lisboa, Ana Sofia Fernandes, José Manuel Fonseca, Chris Bajdik, Elia Biganzoli
Assessment of benefit vs. risk of drug therapy: The potential for outcome analysis with flexible models. IJCNN 2010: 1-8 - [c36]Enrique Romero, Ana Sofia Fernandes, Tingting Mu, Paulo J. G. Lisboa:
Cohort-based kernel visualisation with scatter matrices. IJCNN 2010: 1-8
2000 – 2009
- 2009
- [j31]Thorsteinn S. Rögnvaldsson
How to find simple and accurate rules for viral protease cleavage specificities. BMC Bioinform. 10 (2009) - [j30]D. Faulke, Terence A. Etchells, M. J. Harrison, Paulo J. G. Lisboa:
Determination of mode of ventilation using OSRE. Comput. Biol. Medicine 39(11): 1032-1035 (2009) - [j29]Leif E. Peterson, Paulo J. G. Lisboa:
Editorial. Int. J. Knowl. Eng. Soft Data Paradigms 1(3): 193-194 (2009) - [j28]Ana S. Fernandes, José Manuel Fonseca, Ian H. Jarman, Terence A. Etchells, Paulo J. G. Lisboa, Elia Biganzoli, Chris Bajdik:
Evaluation of missing data imputation in longitudinal cohort studies in breast cancer survival. Int. J. Knowl. Eng. Soft Data Paradigms 1(3): 257-276 (2009) - [j27]Azzam Fouad George Taktak, Antonio Eleuteri, Min S. Hane Aung, Paulo J. G. Lisboa, Laurence Desjardins, Bertil E. Damato:
Survival analysis in cancer using a partial logistic neural network model with Bayesian regularisation framework: a validation study. Int. J. Knowl. Eng. Soft Data Paradigms 1(3): 277-295 (2009) - [j26]Ali Al-Fayadh
Novel hybrid classified vector quantization using discrete cosine transform for image compression. J. Electronic Imaging 18(2): 023003 (2009) - [j25]Paulo J. G. Lisboa, Terence A. Etchells, Ian H. Jarman, Corneliu T. C. Arsene, Min S. Hane Aung, Antonio Eleuteri, Azzam Fouad George Taktak, Federico Ambrogi
Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination. IEEE Trans. Neural Networks 20(9): 1403-1416 (2009) - [c35]Ana S. Fernandes, Davide Bacciu
Different Methodologies for Patient Stratification Using Survival Data. CIBB 2009: 276-290 - [c34]Abir Jaafar Hussain, Dhiya Al-Jumeily
Adaptive Classified Vector Quantisation of Non-orthogonal Representations of Images and its Application to Image Compression. CICSyN 2009: 386-391 - [c33]Davide Bacciu
Patient stratification with competing risks by multivariate Fisher distance. IJCNN 2009: 213-220 - [c32]Ana S. Fernandes, Ian H. Jarman, Terence A. Etchells, José Manuel Fonseca, Elia Biganzoli
Stratification Methodologies for Neural Networks Models of Survival. IWANN (1) 2009: 989-996 - [c31]Ming Li, M. Benjamin Dias, Ian H. Jarman, Wael El-Deredy
Grocery shopping recommendations based on basket-sensitive random walk. KDD 2009: 1215-1224 - [p2]Paulo J. G. Lisboa, Ian H. Jarman, Terence A. Etchells, Federico Ambrogi
Short-term time-to-event model of response to treatment following the GIMEMA protocol for Acute Myeloid Leukaemia. Computational Intelligence and Bioengineering 2009: 81-93 - [r1]José David Martín-Guerrero, Emilio Soria-Olivas, Paulo J. G. Lisboa, Antonio J. Serrano-López:
An AI Walk from Pharmacokinetics to A Marketing. Encyclopedia of Artificial Intelligence 2009: 71-75 - 2008
- [j24]Ian H. Jarman, Terence A. Etchells, José David Martín-Guerrero
An integrated framework for risk profiling of breast cancer patients following surgery. Artif. Intell. Medicine 42(3): 165-188 (2008) - [j23]Abir Jaafar Hussain, Adam Knowles, Paulo J. G. Lisboa, Wael El-Deredy
Financial time series prediction using polynomial pipelined neural networks. Expert Syst. Appl. 35(3): 1186-1199 (2008) - [j22]Paulo J. G. Lisboa, Terence A. Etchells, Ian H. Jarman, M. S. Hane Aung, Sylvie Chabaud, Thomas Bachelot, David Pérol, Thérèse Gargi, Valérie Bourdès, Stéphane Bonnevay, Sylvie Négrier:
Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer. Neural Networks 21(2-3): 414-426 (2008) - [j21]Paulo J. G. Lisboa, Ian O. Ellis
Cluster-based visualisation with scatter matrices. Pattern Recognit. Lett. 29(13): 1814-1823 (2008) - [c30]Ali Al-Fayadh
An Adaptive Hybrid Classified Vector Quantisation and its Application to Image Compression. EMS 2008: 253-256 - [c29]Alfredo Vellido, Elia Biganzoli, Paulo J. G. Lisboa:
Machine learning in cancer research: implications for personalised medicine. ESANN 2008: 55-64 - [c28]Azzam F. Taktak, Antonio Eleuteri, M. S. Hane Aung, Paulo J. G. Lisboa, Laurence Desjardins, Bertil E. Damato:
External Validation of a Bayesian Neural Network Model in Survival Analysis. ICMLA 2008: 607-612 - [c27]Paulo J. G. Lisboa, Enrique Romero, Alfredo Vellido, Margarida Julià-Sapé
Classification, Dimensionality Reduction, and Maximally Discriminatory Visualization of a Multicentre 1H-MRS Database of Brain Tumors. ICMLA 2008: 613-618 - [c26]Ana S. Fernandes, Ian H. Jarman, Terence A. Etchells, José Manuel Fonseca, Elia Biganzoli
Missing Data Imputation in Longitudinal Cohort Studies: Application of PLANN-ARD in Breast Cancer Survival. ICMLA 2008: 644-649 - [c25]Ali Al-Fayadh
An adaptive hybrid image compression method and its application to medical images. ISBI 2008: 237-240 - [c24]Davide Bacciu, Elia Biganzoli, Paulo J. G. Lisboa, Antonina Starita:
Are Model-Based Clustering and Neural Clustering Consistent? A Case Study from Bioinformatics. KES (2) 2008: 181-188 - [c23]Terence A. Etchells, Ana S. Fernandes, Ian H. Jarman, José Manuel Fonseca, Paulo J. G. Lisboa:
Stratification of Severity of Illness Indices: A Case Study for Breast Cancer Prognosis. KES (2) 2008: 214-221 - [c22]M. Benjamin Dias, Dominique Locher, Ming Li, Wael El-Deredy
The value of personalised recommender systems to e-business: a case study. RecSys 2008: 291-294 - 2007
- [j20]Azzam F. Taktak, Laura Antolini, Min S. Hane Aung, Patrizia Boracchi
Double-blind evaluation and benchmarking of survival models in a multi-centre study. Comput. Biol. Medicine 37(8): 1108-1120 (2007) - [j19]José David Martín-Guerrero
An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal. Expert Syst. Appl. 33(3): 743-753 (2007) - [j18]A. C. Fisher, Wael El-Deredy
Removal of eye movement artefacts from single channel recordings of retinal evoked potentials using synchronous dynamical embedding and independent component analysis. Medical Biol. Eng. Comput. 45(1): 69-77 (2007) - [c21]Carmen M. Sordo-Garcia, M. Benjamin Dias, Ming Li, Wael El-Deredy, Paulo J. G. Lisboa:
Evaluating Retail Recommender Systems via Retrospective Data: Lessons Learnt from a Live-Intervention Study. DMIN 2007: 197-206 - [c20]Paulo J. G. Lisboa, Elia Biganzoli
Assessing flexible models and rule extraction from censored survival data. IJCNN 2007: 1663-1668 - [c19]Paulo J. G. Lisboa, Terence A. Etchells, Ian H. Jarman, M. S. Hane Aung, Sylvie Chabaud, Thomas Bachelot, David Pérol, Thérèse Gargi, Valérie Bourdès, Stéphane Bonnevay, Sylvie Négrier:
Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer. IJCNN 2007: 2533-2538 - [c18]M. S. Hane Aung, Paulo J. G. Lisboa, Terence A. Etchells, Antonia C. Testa
Comparing Analytical Decision Support Models Through Boolean Rule Extraction: A Case Study of Ovarian Tumour Malignancy. ISNN (2) 2007: 1177-1186 - [c17]Alfredo Vellido, Paulo J. G. Lisboa:
Neural Networks and Other Machine Learning Methods in Cancer Research. IWANN 2007: 964-971 - [c16]Paulo J. G. Lisboa, Ian H. Jarman, Terence A. Etchells, Phillip Ramsey:
A Prototype Integrated Decision Support System for Breast Cancer Oncology. IWANN 2007: 996-1003 - [c15]Ming Li, M. Benjamin Dias, Wael El-Deredy
A probabilistic model for item-based recommender systems. RecSys 2007: 129-132 - 2006
- [j17]Alfredo Vellido, Paulo J. G. Lisboa:
Handling outliers in brain tumour MRS data analysis through robust topographic mapping. Comput. Biol. Medicine 36(10): 1049-1063 (2006) - [j16]Alfredo Vellido, Paulo J. G. Lisboa, Dolores Vicente:
Robust analysis of MRS brain tumour data using t-GTM. Neurocomputing 69(7-9): 754-768 (2006) - [j15]Paulo J. G. Lisboa, Azzam Fouad George Taktak:
The use of artificial neural networks in decision support in cancer: A systematic review. Neural Networks 19(4): 408-415 (2006) - [j14]Terence A. Etchells, Paulo J. G. Lisboa:
Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach. IEEE Trans. Neural Networks 17(2): 374-384 (2006) - [c14]Abir Jaafar Hussain, Adam Knowles, Paulo J. G. Lisboa, Wael El-Deredy, Dhiya Al-Jumeily
Polynomial Pipelined Neural Network and Its Application to Financial Time Series Prediction. Australian Conference on Artificial Intelligence 2006: 597-606 - [c13]Terence A. Etchells, Àngela Nebot, Alfredo Vellido, Paulo J. G. Lisboa, Francisco Mugica:
Learning what is important: feature selection and rule extraction in a virtual course. ESANN 2006: 401-406 - [c12]Shail Patel, Ogi Bataveljic, Paulo J. G. Lisboa, Chris Hawkins, Rohithari Rajan:
iShakti - Crossing the Digital Divide in Rural India. Web Intelligence 2006: 1061-1065 - 2005
- [j13]Andreas Lindemann, Christian L. Dunis, Paulo J. G. Lisboa:
Level estimation, classification and probability distribution architectures for trading the EUR/USD exchange rate. Neural Comput. Appl. 14(3): 256-271 (2005) - [c11]Alfredo Vellido, Paulo J. G. Lisboa, Dolores Vicente:
Handling outliers and missing data in brain tumour clinical assessment using t-GTM. ESANN 2005: 121-126 - [c10]Alfredo Vellido, Paulo J. G. Lisboa:
Functional topographic mapping for robust handling of outliers in brain tumour data. ESANN 2005: 133-138 - 2004
- [c9]Paulo J. G. Lisboa, Shail Patel:
Cluster-Based Visualisation of Marketing Data. IDEAL 2004: 552-558 - 2003
- [j12]Paulo J. G. Lisboa, H. Wong, P. Harris, R. Swindell:
A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer. Artif. Intell. Medicine 28(1): 1-25 (2003) - [j11]Alfredo Vellido, Wael El-Deredy
Selective smoothing of the generative topographic mapping. IEEE Trans. Neural Networks 14(4): 847-852 (2003) - 2002
- [j10]Paulo J. G. Lisboa, Terence A. Etchells, Dave C. Pountney:
Minimal MLPs do not model the XOR logic. Neurocomputing 48(1-4): 1033-1037 (2002) - [j9]Paulo J. G. Lisboa:
A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks 15(1): 11-39 (2002) - [c8]Neil Coombes, Roger Payne, Paulo Lisboa:
Comparison of Nested Simulated Annealing and Reactive Tabu Search for Efficient Experimental Designs with Correlated Data. COMPSTAT 2002: 249-254 - 2001
- [j8]Alfredo Vellido, Paulo J. G. Lisboa:
An Electronic Commerce Application of the Bayesian Framework for MLPs: The Effect of Marginalisation and ARD. Neural Comput. Appl. 10(1): 3-11 (2001) - 2000
- [j7]Alfredo Vellido, Paulo J. G. Lisboa, Karon Meehan:
The generative topographic mapping as a principal model for data visualization and market segmentation: an electronic commerce case. Int. J. Comput. Syst. Signals 1(2): 119-138 (2000) - [j6]Alfredo Vellido, Paulo J. G. Lisboa, Karon Meehan:
Quantitative Characterization and Prediction of On-Line Purchasing Behavior: A Latent Variable Approach. Int. J. Electron. Commer. 4(4): 83-104 (2000) - [j5]Paulo J. G. Lisboa, Alfredo Vellido, H. Wong:
Bias reduction in skewed binary classification with Bayesian neural networks. Neural Networks 13(4-5): 407-410 (2000) - [c7]Paulo J. G. Lisboa, Alfredo Vellido, H. Wong:
Outstanding Issues for Clinical Decision Support with Neural Networks. ANNIMAB 2000: 63-71 - [c6]Adrian K. Agogino, Joydeep Ghosh, Stavros J. Perantonis, Vassilis Virvilis, Sergios Petridis, Paulo J. G. Lisboa:
The Role of Multiple, Linear-Projection Based Visualization Techniques in RBF-Based Classification of High Dimensional Data. IJCNN (3) 2000: 47-52 - [c5]Alfredo Vellido, Paulo J. G. Lisboa, Karon Meehan:
Segmenting the e-Commerce Market Using the Generative Topographic Mapping. MICAI 2000: 470-481 - [p1]Paulo J. G. Lisboa, Emmanuel C. Ifeachor, Piotr S. Szczepaniak:
Introduction. Artificial Neural Networks in Biomedicine 2000: 1-7 - [e1]Paulo J. G. Lisboa, Emmanuel C. Ifeachor, Piotr S. Szczepaniak:
Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing, Springer 2000, ISBN 978-1-85233-005-7 [contents]
1990 – 1999
- 1999
- [j4]Kevin J. Glossop, Paulo J. G. Lisboa, P. C. Russell, A. Siddans, G. R. Jones:
An Implementation of the Hough Transformation for the Identification and Labelling of Fixed Period Sinusoidal Curves. Comput. Vis. Image Underst. 74(1): 96-100 (1999) - [c4]H. Wong, P. Harris, Paulo J. G. Lisboa, S. P. J. Kirby, R. Swindell:
Dealing with censorship in neural network models. IJCNN 1999: 3702-3706 - 1997
- [c3]Maxim A. Grudin, David M. Harvey, Leonid I. Timchenko, Paulo J. G. Lisboa:
Fast face recognition method using a multistage hierarchical network. ICASSP 1997: 2545-2548 - [c2]Paulo J. G. Lisboa, Neil M. Branston, Wael El-Deredy, Alfredo Vellido:
Tissue characterisation with NMR spectroscopy: current state and future prospects for the application of neural networks analysis. ICNN 1997: 1385-1390 - 1996
- [j3]V. S. Kodogiannis
Neural network modelling and control for underwater vehicles. Artif. Intell. Eng. 10(3): 203-212 (1996) - [j2]J. B. Gomm, D. Williams, J. T. Evans, S. K. Doherty, Paulo J. G. Lisboa:
Enhancing the non-linear modelling capabilities of MLP neural networks using spread encoding. Fuzzy Sets Syst. 79(1): 113-126 (1996) - 1993
- [b1]Paulo J. G. Lisboa, Malcolm J. Taylor:
Techniques and applications of neural networks. Ellis Horwood workshop series, Ellis Horwood 1993, ISBN 978-0-13-062183-2, pp. 1-307 - 1992
- [j1]Stavros J. Perantonis, Paulo J. G. Lisboa:
Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers. IEEE Trans. Neural Networks 3(2): 241-251 (1992) - [c1]Paulo J. G. Lisboa, M. Mallaiah:
The role of local scale and orientation in feature location using neural nets. ICPR (3) 1992: 672-675
Coauthor Index
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