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Davide Anguita
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2020 – today
- 2024
- [j69]Luca Oneto
, Sandro Ridella, Davide Anguita
Towards algorithms and models that we can trust: A theoretical perspective. Neurocomputing 592: 127798 (2024) - [j68]Giovanni Donghi
Investigating over-parameterized randomized graph networks. Neurocomputing 606: 128281 (2024) - [i1]Daniele Angioni, Luca Demetrio, Maura Pintor, Luca Oneto, Davide Anguita, Battista Biggio, Fabio Roli:
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates. CoRR abs/2402.17390 (2024) - 2023
- [b1]Giuliano Donzellini, Luca Oneto, Domenico Ponta, Davide Anguita:
Introduzione al Progetto di Sistemi Digitali, 2a Edition. Springer 2023, ISBN 978-88-470-4025-0, pp. 1-572 - [j67]Luca Oneto
Do we really need a new theory to understand over-parameterization? Neurocomputing 543: 126227 (2023) - [j66]Olga Valenzuela
New Advances in Artificial Neural Networks and Machine Learning Techniques. Neural Process. Lett. 55(5): 5269-5272 (2023) - [c97]Danilo Franco, Luca Oneto, Davide Anguita:
Mitigating Robustness Bias: Theoretical Results and Empirical Evidences. ESANN 2023 - [c96]Luca Oneto, Sandro Ridella, Davide Anguita:
Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective. ESANN 2023 - [c95]Danilo Franco
Fair Empirical Risk Minimization Revised. IWANN (1) 2023: 29-42 - [c94]Guido Parodi, Luca Oneto
Physics Informed Data Driven Techniques for Power Flow Analysis. SSCI 2023: 33-40 - 2022
- [j65]Danilo Franco
Deep fair models for complex data: Graphs labeling and explainable face recognition. Neurocomputing 470: 318-334 (2022) - [j64]Luca Oneto
The benefits of adversarial defense in generalization. Neurocomputing 505: 125-141 (2022) - [j63]Miltiadis Kalikatzarakis, Andrea Coraddu
Optimizing Fuel Consumption in Thrust Allocation for Marine Dynamic Positioning Systems. IEEE Trans Autom. Sci. Eng. 19(1): 122-142 (2022) - [c93]Luca Oneto, Simone Minisi, Andrea Garrone, Renzo Canepa, Carlo Dambra
Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets. ESANN 2022 - [c92]Luca Oneto, Sandro Ridella, Davide Anguita:
Do We Really Need a New Theory to Understand the Double-Descent? ESANN 2022 - [c91]Vincenzo Stefano D'Amato
The Importance of Multiple Temporal Scales in Motion Recognition: when Shallow Model can Support Deep Multi Scale Models. IJCNN 2022: 1-10 - [c90]Vincenzo Stefano D'Amato
The Importance of Multiple Temporal Scales in Motion Recognition: from Shallow to Deep Multi Scale Models. IJCNN 2022: 1-9 - [c89]Andrea Garrone, Simone Minisi, Luca Oneto
Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment. SYSINT 2022: 74-83 - 2021
- [j62]Danilo Franco
Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition. Entropy 23(8): 1047 (2021) - [c88]Vincenzo Stefano D'Amato
Keep it Simple: Handcrafting Feature and Tuning Random Forests and XGBoost to face the Affective Movement Recognition Challenge 2021. ACII (Workshops and Demos) 2021: 1-7 - [c87]Gianluca Boleto, Luca Oneto
In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models. ESANN 2021 - [c86]Luca Oneto
The Benefits of Adversarial Defence in Generalisation. ESANN 2021 - [c85]Danilo Franco
Learn and Visually Explain Deep Fair Models: an Application to Face Recognition. IJCNN 2021: 1-10 - [c84]Vincenzo Stefano D'Amato
Accuracy and Intrusiveness in Data-Driven Violin Players Skill Levels Prediction: MOCAP Against MYO Against KINECT. IWANN (2) 2021: 367-379 - 2020
- [j61]Cecilio Angulo
Bridging Cognitive Models and Recommender Systems. Cogn. Comput. 12(2): 426-427 (2020) - [j60]Vincenzo Stefano D'Amato
Understanding Violin Players' Skill Level Based on Motion Capture: a Data-Driven Perspective. Cogn. Comput. 12(6): 1356-1369 (2020) - [j59]Luca Oneto
A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks. Int. J. Data Sci. Anal. 9(1): 95-111 (2020) - [j58]Joaquín Luque
Spectral Analysis of Electricity Demand Using Hilbert-Huang Transform. Sensors 20(10): 2912 (2020) - [c83]Luca Oneto, Sandro Ridella, Davide Anguita:
Improving the Union Bound: a Distribution Dependent Approach. ESANN 2020: 423-428 - [e2]Luca Oneto
Recent Advances in Big Data and Deep Learning, Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL 2019, held at Sestri Levante, Genova, Italy 16-18 April 2019. Springer 2020, ISBN 978-3-030-16840-7 [contents] - [e1]Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita:
Recent Trends in Learning From Data - Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL 2019). Studies in Computational Intelligence 896, Springer 2020, ISBN 978-3-030-43882-1 [contents]
2010 – 2019
- 2019
- [j57]Alessandro Lulli, Luca Oneto
Mining Big Data with Random Forests. Cogn. Comput. 11(2): 294-316 (2019) - [j56]Luca Oneto
Local Rademacher Complexity Machine. Neurocomputing 342: 24-32 (2019) - [c82]Luca Oneto
Introduction. INNSBDDL (Tutorials) 2019: 1-4 - [c81]Roberto Spigolon, Luca Oneto, Dimitar Anastasovski, Nadia Fabrizio, Marie Swiatek, Renzo Canepa, Davide Anguita:
Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies. INNSBDDL 2019: 120-125 - [c80]Luca Oneto, Irene Buselli
Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability. INNSBDDL 2019: 136-141 - [c79]Luca Oneto, Irene Buselli
Train Overtaking Prediction in Railway Networks: A Big Data Perspective. INNSBDDL 2019: 142-151 - [c78]Francesca Cipollini
Cavitation Noise Spectra Prediction with Hybrid Models. INNSBDDL 2019: 152-157 - [c77]Udo Schlegel, Wolfgang Jentner
Visual Analytics for Supporting Conflict Resolution in Large Railway Networks. INNSBDDL 2019: 206-215 - [c76]Alice Consilvio
Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support. MT-ITS 2019: 1-10 - 2018
- [j55]Luca Oneto
Train Delay Prediction Systems: A Big Data Analytics Perspective. Big Data Res. 11: 54-64 (2018) - [j54]Luca Oneto
Randomized learning: Generalization performance of old and new theoretically grounded algorithms. Neurocomputing 298: 21-33 (2018) - [j53]Luca Oneto
Multilayer Graph Node Kernels: Stacking While Maintaining Convexity. Neural Process. Lett. 48(2): 649-667 (2018) - [j52]Francesca Cipollini
Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback. Reliab. Eng. Syst. Saf. 177: 12-23 (2018) - [j51]Luca Oneto
Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids. IEEE Syst. J. 12(3): 2842-2853 (2018) - [j50]Luca Oneto
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4660-4671 (2018) - [c75]Alessandro Lulli, Luca Oneto
Large-Scale Railway Networks Train Movements: A Dynamic, Interpretable, and Robust Hybrid Data Analytics System. DSAA 2018: 371-380 - [c74]Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita:
Emerging trends in machine learning: beyond conventional methods and data. ESANN 2018 - [c73]Luca Oneto, Sandro Ridella, Davide Anguita:
Local Rademacher Complexity Machine. ESANN 2018 - [c72]Francesca Cipollini
Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents. INNS Conference on Big Data 2018: 42-51 - 2017
- [j49]Luca Oneto
Semi-supervised Learning for Affective Common-Sense Reasoning. Cogn. Comput. 9(1): 18-42 (2017) - [j48]Luca Oneto
SLT-Based ELM for Big Social Data Analysis. Cogn. Comput. 9(2): 259-274 (2017) - [j47]Luca Oneto
Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing 268: 4-16 (2017) - [j46]Luca Oneto
Differential privacy and generalization: Sharper bounds with applications. Pattern Recognit. Lett. 89: 31-38 (2017) - [j45]Isah Abdullahi Lawal, Fabio Poiesi
Support Vector Motion Clustering. IEEE Trans. Circuits Syst. Video Technol. 27(11): 2395-2408 (2017) - [j44]Luca Oneto
Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout. IEEE Trans. Syst. Man Cybern. Syst. 47(10): 2754-2767 (2017) - [c71]Alessandro Lulli, Luca Oneto
Crack random forest for arbitrary large datasets. IEEE BigData 2017: 706-715 - [c70]Luca Oneto, Sandro Ridella, Davide Anguita:
Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions. ESANN 2017 - [c69]Luca Oneto, Anna Siri, Gianvittorio Luria, Davide Anguita:
Dropout Prediction at University of Genoa: a Privacy Preserving Data Driven Approach. ESANN 2017 - [c68]Alessandro Lulli, Luca Oneto
ReForeSt: Random Forests in Apache Spark. ICANN (2) 2017: 331-339 - [c67]Luca Oneto
Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction. ICANN (2) 2017: 385-393 - [c66]Luca Oneto
Deep graph node kernels: A convex approach. IJCNN 2017: 316-323 - 2016
- [j43]Luca Oneto
Statistical Learning Theory and ELM for Big Social Data Analysis. IEEE Comput. Intell. Mag. 11(3): 45-55 (2016) - [j42]Jorge Luis Reyes-Ortiz
Transition-Aware Human Activity Recognition Using Smartphones. Neurocomputing 171: 754-767 (2016) - [j41]Mehrnoosh Vahdat, Luca Oneto
Can machine learning explain human learning? Neurocomputing 192: 14-28 (2016) - [j40]Luca Oneto
Tikhonov, Ivanov and Morozov regularization for support vector machine learning. Mach. Learn. 103(1): 103-136 (2016) - [j39]Luca Oneto
A local Vapnik-Chervonenkis complexity. Neural Networks 82: 62-75 (2016) - [j38]Luca Oneto
Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates. Neural Process. Lett. 43(2): 567-602 (2016) - [j37]Luca Oneto
PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis. Pattern Recognit. Lett. 80: 200-207 (2016) - [j36]Luca Oneto
Learning Hardware-Friendly Classifiers Through Algorithmic Stability. ACM Trans. Embed. Comput. Syst. 15(2): 23:1-23:29 (2016) - [c65]Luca Oneto
Advanced Analytics for Train Delay Prediction Systems by Including Exogenous Weather Data. DSAA 2016: 458-467 - [c64]Mehrnoosh Vahdat, Maira B. Carvalho
Learning Analytics for a Puzzle Game to Discover the Puzzle-Solving Tactics of Players. EC-TEL 2016: 673-677 - [c63]Luca Oneto, Nicolò Navarin, Michele Donini, Fabio Aiolli, Davide Anguita:
Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. ESANN 2016 - [c62]Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. ESANN 2016 - [c61]Luca Oneto, Sandro Ridella, Davide Anguita:
Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data. ESANN 2016 - [c60]Ilenia Orlandi, Luca Oneto, Davide Anguita:
Random Forests Model Selection. ESANN 2016 - [c59]Luca Oneto
Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics. INNS Conference on Big Data 2016: 139-150 - [c58]Luca Oneto
Vessel monitoring and design in industry 4.0: A data driven perspective. RTSI 2016: 1-6 - [p1]Luca Oneto
Learning Hardware Friendly Classifiers Through Algorithmic Risk Minimization. Advances in Neural Networks 2016: 403-413 - 2015
- [j35]Luca Oneto
Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency. Neurocomputing 169: 225-235 (2015) - [j34]Luca Oneto
Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples. Neural Networks 65: 115-125 (2015) - [j33]Luca Oneto
Fully Empirical and Data-Dependent Stability-Based Bounds. IEEE Trans. Cybern. 45(9): 1913-1926 (2015) - [c57]Luca Oneto
Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems. IEEE BigData 2015: 1436-1445 - [c56]Mehrnoosh Vahdat, Luca Oneto
A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator. EC-TEL 2015: 352-366 - [c55]Luca Oneto, Bernardo Pilarz, Alessandro Ghio, Davide Anguita:
Model Selection for Big Data: Algorithmic Stability and Bag of Little Bootstraps on GPUs. ESANN 2015 - [c54]Mehrnoosh Vahdat, Alessandro Ghio, Luca Oneto, Davide Anguita, Mathias Funk, Matthias Rauterberg:
Advances in learning analytics and educational data mining. ESANN 2015 - [c53]Mehrnoosh Vahdat, Luca Oneto, Alessandro Ghio, Davide Anguita, Mathias Funk, Matthias Rauterberg:
Human Algorithmic Stability and Human Rademacher Complexity. ESANN 2015 - [c52]Luca Oneto
Shrinkage learning to improve SVM with hints. IJCNN 2015: 1-9 - [c51]Luca Oneto
Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters. IJCNN 2015: 1-4 - [c50]Luca Oneto
Fast convergence of extended Rademacher Complexity bounds. IJCNN 2015: 1-10 - [c49]Jorge Luis Reyes-Ortiz, Luca Oneto
Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf. INNS Conference on Big Data 2015: 121-130 - [c48]Emanuele Fumeo, Luca Oneto
Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis. INNS Conference on Big Data 2015: 437-446 - [d4]Jorge Luis Reyes-Ortiz, Davide Anguita, Luca Oneto, Xavier Parra:
Smartphone-Based Recognition of Human Activities and Postural Transitions. UCI Machine Learning Repository, 2015 - [d3]Mehrnoosh Vahdat, Luca Oneto, Davide Anguita, Mathias Funk, Matthias Rauterberg:
Educational Process Mining (EPM): A Learning Analytics Data Set. UCI Machine Learning Repository, 2015 - 2014
- [j32]Davide Anguita
Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers. Pattern Recognit. Lett. 37: 210-219 (2014) - [j31]Davide Anguita
A Deep Connection Between the Vapnik-Chervonenkis Entropy and the Rademacher Complexity. IEEE Trans. Neural Networks Learn. Syst. 25(12): 2202-2211 (2014) - [c47]Mehrnoosh Vahdat, Luca Oneto
A Learning Analytics Methodology to Profile Students Behavior and Explore Interactions with a Digital Electronics Simulator. EC-TEL 2014: 596-597 - [c46]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
Learning with few bits on small-scale devices: From regularization to energy efficiency. ESANN 2014 - [c45]Jorge Luis Reyes-Ortiz, Luca Oneto
Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions. ICANN 2014: 177-184 - [c44]Luca Oneto
Out-of-Sample Error Estimation: The Blessing of High Dimensionality. ICDM Workshops 2014: 637-644 - [c43]Davide Anguita
Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities. IJCNN 2014: 3916-3921 - [d2]Andrea Coraddu, Luca Oneto, Alessandro Ghio, Stefano Savio, Davide Anguita, Massimo Figari:
Condition Based Maintenance of Naval Propulsion Plants. UCI Machine Learning Repository, 2014 - 2013
- [j30]Davide Anguita, Luca Ghelardoni, Alessandro Ghio, Sandro Ridella:
A Survey of old and New Results for the Test Error Estimation of a Classifier. J. Artif. Intell. Soft Comput. Res. 3(4): 229 (2013) - [j29]Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz:
Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. J. Univers. Comput. Sci. 19(9): 1295-1314 (2013) - [j28]Luca Oneto
An improved analysis of the Rademacher data-dependent bound using its self bounding property. Neural Networks 44: 107-111 (2013) - [j27]Luca Ghelardoni, Alessandro Ghio, Davide Anguita
Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression. IEEE Trans. Smart Grid 4(1): 549-556 (2013) - [c42]Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz:
A Public Domain Dataset for Human Activity Recognition using Smartphones. ESANN 2013 - [c41]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
A Learning Machine with a Bit-Based Hypothesis Space. ESANN 2013 - [c40]Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra, Davide Anguita, Joan Cabestany, Andreu Català:
Human Activity and Motion Disorder Recognition: towards smarter Interactive Cognitive Environments. ESANN 2013 - [c39]Davide Anguita
Training Computationally Efficient Smartphone-Based Human Activity Recognition Models. ICANN 2013: 426-433 - [c38]Davide Anguita
A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers. ICANN 2013: 434-441 - [c37]Davide Anguita
Some results about the Vapnik-Chervonenkis entropy and the rademacher complexity. IJCNN 2013: 1-8 - [c36]Davide Anguita
A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space. IJCNN 2013: 1-10 - 2012
- [j26]Davide Anguita
In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine. Neural Process. Lett. 36(3): 275-283 (2012) - [j25]Davide Anguita
In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines. IEEE Trans. Neural Networks Learn. Syst. 23(9): 1390-1406 (2012) - [c35]Davide Anguita, Luca Ghelardoni, Alessandro Ghio, Luca Oneto, Sandro Ridella:
The 'K' in K-fold Cross Validation. ESANN 2012 - [c34]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
Structural Risk Minimization and Rademacher Complexity for Regression. ESANN 2012 - [c33]Alessandro Ghio, Davide Anguita
Nested Sequential Minimal Optimization for Support Vector Machines. ICANN (2) 2012: 156-163 - [c32]Luca Oneto
Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets. ICANN (2) 2012: 491-498 - [c31]Davide Anguita
Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine. IWAAL 2012: 216-223 - [d1]Jorge Luis Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra:
Human Activity Recognition Using Smartphones. UCI Machine Learning Repository, 2012 - 2011
- [j24]Davide Anguita
Maximal Discrepancy for Support Vector Machines. Neurocomputing 74(9): 1436-1443 (2011) - [j23]Davide Anguita
A FPGA Core Generator for Embedded Classification Systems. J. Circuits Syst. Comput. 20(2): 263-282 (2011) - [c30]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
Maximal Discrepancy vs. Rademacher Complexity for error estimation. ESANN 2011 - [c29]Davide Anguita
Test error bounds for classifiers: A survey of old and new results. FOCI 2011: 80-87 - [c28]Davide Anguita
In-sample model selection for Support Vector Machines. IJCNN 2011: 1154-1161 - [c27]Davide Anguita
Selecting the hypothesis space for improving the generalization ability of Support Vector Machines. IJCNN 2011: 1169-1176 - [c26]Luca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella:
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers. NIPS 2011: 585-593 - 2010
- [j22]Sergio Decherchi
Using unsupervised analysis to constrain generalization bounds for support vector classifiers. IEEE Trans. Neural Networks 21(3): 424-438 (2010) - [c25]Davide Anguita, Alessandro Ghio, Sandro Ridella:
Maximal Discrepancy for Support Vector Machines. ESANN 2010 - [c24]Davide Anguita
Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory. IJCNN 2010: 1-8
2000 – 2009
- 2009
- [j21]Bogdan Gabrys, Davide Anguita
Nature-inspired learning and adaptive systems. Nat. Comput. 8(2): 197-198 (2009) - [c23]Davide Anguita, Alessandro Ghio, Sandro Ridella, Dario Sterpi:
K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines. DMIN 2009: 291-297 - 2008
- [j20]Cecilio Angulo
Support vector machines for interval discriminant analysis. Neurocomputing 71(7-9): 1220-1229 (2008) - [j19]Davide Anguita
A support vector machine with integer parameters. Neurocomputing 72(1-3): 480-489 (2008) - [c22]Davide Anguita, Davide Brizzolara, Alessandro Ghio, Giancarlo Parodi:
Smart plankton - a new generation of underwater wireless sensor network. ALIFE 2008: 745 - [c21]Davide Anguita
Smart Plankton: a Nature Inspired Underwater Wireless Sensor Network. ICNC (7) 2008: 701-705 - [c20]Enrique Alba
Using Variable Neighborhood Search to improve the Support Vector Machine performance in embedded automotive applications. IJCNN 2008: 984-988 - 2007
- [c19]Davide Anguita
A learning machine for resource-limited adaptive hardware. AHS 2007: 571-576 - [c18]Cecilio Angulo, Davide Anguita, Luis González Abril:
Interval discriminant analysis using support vector machines. ESANN 2007: 223-228 - [c17]Davide Anguita
A Hardware-friendly Support Vector Machine for Embedded Automotive Applications. IJCNN 2007: 1360-1364 - 2006
- [j18]Davide Anguita
Feed-Forward Support Vector Machine Without Multipliers. IEEE Trans. Neural Networks 17(5): 1328-1331 (2006) - [c16]Davide Anguita
Testing the Augmented Binary Multiclass SVM on Microarray Data. IJCNN 2006: 1966-1968 - [c15]Davide Anguita
Nature Inspiration for Support Vector Machines. KES (2) 2006: 442-449 - 2005
- [c14]Davide Anguita, Arianna Poggi, Fabio Rivieccio, Anna Marina Scapolla:
Data Mining Tools: From Web to Grid Architectures. EGC 2005: 620-629 - 2004
- [j17]Davide Anguita
Special issue on hardware implementations of soft computing techniques. Appl. Soft Comput. 4(3): 204-205 (2004) - [c13]Davide Anguita, Sandro Ridella, Fabio Rivieccio:
An Algorithm for Reducing the Number of Support Vectors. WIRN 2004: 99-105 - 2003
- [j16]Davide Anguita
Hyperparameter design criteria for support vector classifiers. Neurocomputing 55(1-2): 109-134 (2003) - [j15]Davide Anguita
Neural network learning for analog VLSI implementations of support vector machines: a survey. Neurocomputing 55(1-2): 265-283 (2003) - [j14]Davide Anguita
Quantum optimization for training support vector machines. Neural Networks 16(5-6): 763-770 (2003) - [j13]Davide Anguita
Digital Least Squares Support Vector Machines. Neural Process. Lett. 18(1): 65-72 (2003) - [j12]Davide Anguita
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation. IEEE Trans. Neural Networks 14(5): 993-1009 (2003) - 2002
- [j11]Davide Anguita
SVM performance assessment for the control of injection moulding processes and plasticating extrusion. Int. J. Syst. Sci. 33(9): 723-735 (2002) - [j10]Davide Anguita
Improved neural network for SVM learning. IEEE Trans. Neural Networks 13(5): 1243-1244 (2002) - [c12]Davide Anguita
MDL Based Model Selection for Relevance Vector Regression. ICANN 2002: 468-473 - [c11]Davide Anguita, Sandro Ridella, Fabio Rivieccio, Rodolfo Zunino:
Automatic Hyperparameter Tuning for Support Vector Machines. ICANN 2002: 1345-1350 - 2001
- [c10]Davide Anguita, Maurizio Valle:
Perspectives on dedicated hardware implementations. ESANN 2001: 45-56 - 2000
- [j9]Davide Anguita
Digital VLSI Algorithms and Architectures for Support Vector Machines. Int. J. Neural Syst. 10(3): 159-170 (2000) - [j8]Davide Anguita
A case study of a distributed high-performance computing system for neurocomputing. J. Syst. Archit. 46(5): 429-438 (2000) - [j7]Davide Anguita
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap. Neural Process. Lett. 11(1): 51-58 (2000) - [c9]Davide Anguita
Fast Training of Support Vector Machines for Regression. IJCNN (5) 2000: 210-216
1990 – 1999
- 1999
- [j6]Davide Anguita
Worst case analysis of weight inaccuracy effects in multilayer perceptrons. IEEE Trans. Neural Networks 10(2): 415-418 (1999) - [c8]Davide Anguita, Andrea Boni, Sandro Ridella:
Support Vector Machines: A Comparison of Some Kernel Functions. IIA/SOCO 1999 - [c7]Davide Anguita
A VLSI friendly algorithm for support vector machines. IJCNN 1999: 939-942 - 1998
- [c6]Davide Anguita, Andrea Boni, Marco Chirico, Fabrizio Giudici
High Performance Neurocomputing: Industrial and Medical Applications of the RAIN System. HPCN Europe 1998: 34-43 - 1997
- [c5]Davide Anguita, Marco Chirico, Anna Marina Scapolla, Giancarlo Parodi:
RAIN: Redundant Array of Inexpensive workstations for Neurocomputing. Euro-Par 1997: 1340-1345 - 1996
- [j5]Davide Anguita
Mixing floating- and fixed-point formats for neural network learning on neuroprocessors. Microprocess. Microprogramming 41(10): 757-769 (1996) - [c4]Davide Anguita, Sandro Ridella, Stefano Rovetta, Rodolfo Zunino:
Limiting the effects of weight errors in feedforward networks using interval arithmetic. ICNN 1996: 414-417 - 1995
- [j4]Davide Anguita
Neural structures for visual motion tracking. Mach. Vis. Appl. 8(5): 275-288 (1995) - [j3]Davide Anguita
A heterogeneous and reconfigurable machine-vision system. Mach. Vis. Appl. 8(5): 343-350 (1995) - [c3]Davide Anguita, Filippo Passaggio, Rodolfo Zunino:
Learning in large neural networks. HPCN Europe 1995: 269-274 - [c2]Jean-Marc Adamo, Davide Anguita
Object Oriented Design of a Simulator for Large BP Neural Networks. IWANN 1995: 642-649 - 1994
- [j2]Davide Anguita
An efficient implementation of BP on RISC-based workstations. Neurocomputing 6(1): 57-65 (1994) - [j1]Davide Anguita
Associative structures for vision. Multidimens. Syst. Signal Process. 5(1): 75-96 (1994) - 1991
- [c1]Davide Anguita, Giancarlo Parodi, Domenico Ponta, Rodolfo Zunino:
Transputer-based architectures for associative image classification. SPDP 1991: 241-248
Coauthor Index
[j38] [j35] [j34] [j33] [c55] [c54] [c53] [c52] [c51] [c50] [j32] [j31] [c47] [c46] [c45] [c44] [c43] [d2] [j30] [j29] [j28] [j27] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [j26] [j25] [c35] [c34] [c33] [c32] [c31] [d1] [j24] [j23] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [j19] [c22] [c21] [c20] [c19] [c17]
[j69] [j68] [i1] [b1] [j67] [c97] [c96] [c95] [c94] [j65] [j64] [j63] [c93] [c92] [c91] [c90] [c89] [j62] [c88] [c87] [c86] [c85] [c84] [j60] [j59] [c83] [e2] [e1] [j57] [j56] [c82] [c81] [c80] [c79] [c78] [c77] [c76] [j55] [j54] [j53] [j52] [j51] [j50] [c75] [c74] [c73] [c72] [j49] [j48] [j47] [j46] [j44] [c71] [c70] [c69] [c68] [c67] [c66] [j43] [j42] [j41] [j40] [j39] [j38] [j37] [j36] [c65] [c63] [c62] [c61] [c60] [c59] [c58] [p1] [j35] [j34] [j33] [c57] [c56] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [c48] [d4] [d3] [j32] [j31] [c47] [c46] [c45] [c44] [c43] [d2] [j29] [j28] [c42] [c41] [c39] [c38] [c37] [c36] [j26] [j25] [c35] [c34] [c33] [c32] [c31] [d1] [c30] [c28] [c27] [c26] [c24]
[j69] [j67] [c96] [j64] [c92] [c86] [c83] [j56] [j54] [j50] [c73] [j46] [c70] [j40] [j39] [j38] [j37] [j36] [c61] [j35] [j34] [j33] [c52] [c51] [c50] [j32] [j31] [c46] [c44] [c43] [j30] [j28] [c41] [c38] [c37] [c36] [j26] [j25] [c35] [c34] [c33] [c32] [j24] [j23] [c30] [c29] [c28] [c27] [c26] [j22] [c25] [c24] [c23] [j19] [c20] [c17] [j18] [c16] [c13] [j16] [j14] [j12] [c11] [j9] [j7] [j6] [c8] [c7] [c4]
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