Luca Oneto

Orcid: 0000-0002-8445-395X

According to our database1, Luca Oneto authored at least 180 papers between 2010 and 2024.

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Bibliography

2024
Advances in artificial neural networks, machine learning and computational intelligence.
Neurocomputing, February, 2024

Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing.
Neurocomputing, January, 2024

Towards algorithms and models that we can trust: A theoretical perspective.
Neurocomputing, 2024

Investigating over-parameterized randomized graph networks.
Neurocomputing, 2024

Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis.
CoRR, 2024

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates.
CoRR, 2024

Data-Driven Models for Yacht Hull Resistance Optimization: Exploring Geometric Parameters Beyond the Boundaries of the Delft Systematic Yacht Hull Series.
IEEE Access, 2024

Computationally Aware Surrogate Models for the Hydrodynamic Response Characterization of Floating Spar-Type Offshore Wind Turbine.
IEEE Access, 2024

2023
Do we really need a new theory to understand over-parameterization?
Neurocomputing, July, 2023

On the problem of recommendation for sensitive users and influential items: Simultaneously maintaining interest and diversity.
Knowl. Based Syst., 2023

Physically plausible propeller noise prediction via recursive corrections leveraging prior knowledge and experimental data.
Eng. Appl. Artif. Intell., 2023

Human Movement Datasets: An Interdisciplinary Scoping Review.
ACM Comput. Surv., 2023

Physics Informed Data Driven Techniques for Power Flow Analysis.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

PhishVision: A Deep Learning Based Visual Brand Impersonation Detector for Identifying Phishing Attacks.
Proceedings of the Optimization, Learning Algorithms and Applications, 2023

Fair Empirical Risk Minimization Revised.
Proceedings of the Advances in Computational Intelligence, 2023

Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Mitigating Robustness Bias: Theoretical Results and Empirical Evidences.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Improving Fairness via Intrinsic Plasticity in Echo State Networks.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Short-term Forecast and Long-term Simulation for Accurate Energy Consumption Prediction.
Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023

Introduzione al Progetto di Sistemi Digitali, 2a Edition
Springer, ISBN: 978-88-470-4025-0, 2023

2022
Eleven quick tips for data cleaning and feature engineering.
PLoS Comput. Biol., December, 2022

Identifying the Determinants of Innovation Capability With Machine Learning and Patents.
IEEE Trans. Engineering Management, 2022

Optimizing Fuel Consumption in Thrust Allocation for Marine Dynamic Positioning Systems.
IEEE Trans Autom. Sci. Eng., 2022

The benefits of adversarial defense in generalization.
Neurocomputing, 2022

Advances in artificial neural networks, machine learning and computational intelligence.
Neurocomputing, 2022

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview.
Neurocomputing, 2022

Advances in artificial neural networks, machine learning and computational intelligence.
Neurocomputing, 2022

Deep fair models for complex data: Graphs labeling and explainable face recognition.
Neurocomputing, 2022

Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment.
Proceedings of the Advances in System-Integrated Intelligence, 2022

Data-Driven Methods for Aviation Safety: From Data to Knowledge.
Proceedings of the Advances in System-Integrated Intelligence, 2022

Assessing Emotions in Human-Robot Interaction Based on the Appraisal Theory.
Proceedings of the 31st IEEE International Conference on Robot and Human Interactive Communication, 2022

Deep Learning for the Generation of Heuristics in Answer Set Programming: A Case Study of Graph Coloring.
Proceedings of the Logic Programming and Nonmonotonic Reasoning, 2022

The Importance of Multiple Temporal Scales in Motion Recognition: from Shallow to Deep Multi Scale Models.
Proceedings of the International Joint Conference on Neural Networks, 2022

The Importance of Multiple Temporal Scales in Motion Recognition: when Shallow Model can Support Deep Multi Scale Models.
Proceedings of the International Joint Conference on Neural Networks, 2022

Do We Really Need a New Theory to Understand the Double-Descent?
Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets.
Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Biased Edge Dropout in NIFTY for Fair Graph Representation Learning.
Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Culture Awareness in Intelligent Systems (short paper).
Proceedings of the 9th Italian Workshop on Artificial Intelligence and Robotics co-located with the the 21th International Conference of the Italian Association for Artificial Intelligence, 2022

A Cloud Architecture for Emotion Recognition Based on the Appraisal Theory (short paper).
Proceedings of the 9th Italian Workshop on Artificial Intelligence and Robotics co-located with the the 21th International Conference of the Italian Association for Artificial Intelligence, 2022

2021
An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021

Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma.
Health Informatics J., 2021

Distribution-Dependent Weighted Union Bound.
Entropy, 2021

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition.
Entropy, 2021

Marine dual fuel engines monitoring in the wild through weakly supervised data analytics.
Eng. Appl. Artif. Intell., 2021

Data analytics and clinical feature ranking of medical records of patients with sepsis.
BioData Min., 2021

A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease.
IEEE Access, 2021

A Planning-based Approach for In-Station Train Dispatching.
Proceedings of the Fourteenth International Symposium on Combinatorial Search, 2021

Accuracy and Intrusiveness in Data-Driven Violin Players Skill Levels Prediction: MOCAP Against MYO Against KINECT.
Proceedings of the Advances in Computational Intelligence, 2021

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition.
Proceedings of the International Joint Conference on Neural Networks, 2021

An Efficient Hybrid Planning Framework for In-Station Train Dispatching.
Proceedings of the Computational Science - ICCS 2021, 2021

The Benefits of Adversarial Defence in Generalisation.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Complex Data: Learning Trustworthily, Automatically, and with Guarantees.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

In-Station Train Dispatching: A PDDL+ Planning Approach.
Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, 2021

Keep it Simple: Handcrafting Feature and Tuning Random Forests and XGBoost to face the Affective Movement Recognition Challenge 2021.
Proceedings of the 2021 9th International Conference on Affective Computing and Intelligent Interaction, 2021

2020
Low-Resource Footprint, Data-Driven Malware Detection on Android.
IEEE Trans. Sustain. Comput., 2020

Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy.
Neurocomputing, 2020

Advances in artificial neural networks, machine learning and computational intelligence.
Neurocomputing, 2020

A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks.
Int. J. Data Sci. Anal., 2020

Learning fair models and representations.
Intelligenza Artificiale, 2020

Understanding Violin Players' Skill Level Based on Motion Capture: a Data-Driven Perspective.
Cogn. Comput., 2020

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fair regression via plug-in estimator and recalibration with statistical guarantees.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fair regression with Wasserstein barycenters.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

General Fair Empirical Risk Minimization.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Deep Learning for Cavitating Marine Propeller Noise Prediction at Design Stage.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Towards Online Discovery of Data-Aware Declarative Process Models from Event Streams.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Improving the Union Bound: a Distribution Dependent Approach.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Learning Deep Fair Graph Neural Networks.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Learning Fair and Transferable Representations with Theoretical Guarantees.
Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019
Local Rademacher Complexity Machine.
Neurocomputing, 2019

Advances in artificial neural networks, machine learning and computational intelligence: Selected papers from the 26<sup><i>th</i></sup> European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018).
Neurocomputing, 2019

Simple continuous optimal regions of the space of data.
Neurocomputing, 2019

Technical analysis and sentiment embeddings for market trend prediction.
Expert Syst. Appl., 2019

Learning Fair and Transferable Representations.
CoRR, 2019

Mining Big Data with Random Forests.
Cogn. Comput., 2019

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support.
Proceedings of the 6th International Conference on Models and Technologies for Intelligent Transportation Systems, 2019

Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Visual Analytics for Supporting Conflict Resolution in Large Railway Networks.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Innovation Capability of Firms: A Big Data Approach with Patents.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Introduction.
Proceedings of the Recent Trends in Learning From Data, 2019

Fairness in Machine Learning.
Proceedings of the Recent Trends in Learning From Data, 2019

Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Train Overtaking Prediction in Railway Networks: A Big Data Perspective.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Predicting Future Market Trends: Which Is the Optimal Window?
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Cavitation Noise Spectra Prediction with Hybrid Models.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction.
Proceedings of the International Joint Conference on Neural Networks, 2019

Hybrid Model for Cavitation Noise Spectra Prediction.
Proceedings of the International Joint Conference on Neural Networks, 2019

PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Fairness and Accountability of Machine Learning Models in Railway Market: are Applicable Railway Laws Up to Regulate Them?
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Societal Issues in Machine Learning: When Learning from Data is Not Enough.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Taking Advantage of Multitask Learning for Fair Classification.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018
Model selection and error estimation without the agonizing pain.
WIREs Data Mining Knowl. Discov., 2018

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels.
IEEE Trans. Neural Networks Learn. Syst., 2018

Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids.
IEEE Syst. J., 2018

Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback.
Reliab. Eng. Syst. Saf., 2018

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity.
Neural Process. Lett., 2018

Randomized learning: Generalization performance of old and new theoretically grounded algorithms.
Neurocomputing, 2018

Advances in artificial neural networks, machine learning and computational intelligence.
Neurocomputing, 2018

Train Delay Prediction Systems: A Big Data Analytics Perspective.
Big Data Res., 2018

Ensemble of Technical Analysis and Machine Learning for Market Trend Prediction.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2018

Empirical Risk Minimization Under Fairness Constraints.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents.
Proceedings of the INNS Conference on Big Data and Deep Learning 2018, 2018

Investigating Timing and Impact of News on the Stock Market.
Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Emerging trends in machine learning: beyond conventional methods and data.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Large-Scale Railway Networks Train Movements: A Dynamic, Interpretable, and Robust Hybrid Data Analytics System.
Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics, 2018

2017
Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout.
IEEE Trans. Syst. Man Cybern. Syst., 2017

Differential privacy and generalization: Sharper bounds with applications.
Pattern Recognit. Lett., 2017

Measuring the expressivity of graph kernels through Statistical Learning Theory.
Neurocomputing, 2017

SLT-Based ELM for Big Social Data Analysis.
Cogn. Comput., 2017

Semi-supervised Learning for Affective Common-Sense Reasoning.
Cogn. Comput., 2017

Deep graph node kernels: A convex approach.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

ReForeSt: Random Forests in Apache Spark.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

Dropout Prediction at University of Genoa: a Privacy Preserving Data Driven Approach.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Crack random forest for arbitrary large datasets.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016
Learning Hardware Friendly Classifiers Through Algorithmic Risk Minimization.
Proceedings of the Advances in Neural Networks - Computational Intelligence for ICT, 2016

Learning Hardware-Friendly Classifiers Through Algorithmic Stability.
ACM Trans. Embed. Comput. Syst., 2016

PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis.
Pattern Recognit. Lett., 2016

Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates.
Neural Process. Lett., 2016

A local Vapnik-Chervonenkis complexity.
Neural Networks, 2016

Tikhonov, Ivanov and Morozov regularization for support vector machine learning.
Mach. Learn., 2016

Can machine learning explain human learning?
Neurocomputing, 2016

Transition-Aware Human Activity Recognition Using Smartphones.
Neurocomputing, 2016

Statistical Learning Theory and ELM for Big Social Data Analysis.
IEEE Comput. Intell. Mag., 2016

Vessel monitoring and design in industry 4.0: A data driven perspective.
Proceedings of the 2nd IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow, 2016

Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics.
Proceedings of the Advances in Big Data, 2016

Random Forests Model Selection.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Advanced Analytics for Train Delay Prediction Systems by Including Exogenous Weather Data.
Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

2015
Educational Process Mining (EPM): A Learning Analytics Data Set.
Dataset, September, 2015

Smartphone-Based Recognition of Human Activities and Postural Transitions.
Dataset, July, 2015

Fully Empirical and Data-Dependent Stability-Based Bounds.
IEEE Trans. Cybern., 2015

Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples.
Neural Networks, 2015

Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency.
Neurocomputing, 2015

Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf.
Proceedings of the INNS Conference on Big Data 2015, 2015

Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis.
Proceedings of the INNS Conference on Big Data 2015, 2015

Fast convergence of extended Rademacher Complexity bounds.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Shrinkage learning to improve SVM with hints.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Human Algorithmic Stability and Human Rademacher Complexity.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

Advances in learning analytics and educational data mining.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

Model Selection for Big Data: Algorithmic Stability and Bag of Little Bootstraps on GPUs.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator.
Proceedings of the Design for Teaching and Learning in a Networked World, 2015

Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems.
Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

2014
Condition Based Maintenance of Naval Propulsion Plants.
Dataset, September, 2014

A Deep Connection Between the Vapnik-Chervonenkis Entropy and the Rademacher Complexity.
IEEE Trans. Neural Networks Learn. Syst., 2014

Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers.
Pattern Recognit. Lett., 2014

Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Out-of-Sample Error Estimation: The Blessing of High Dimensionality.
Proceedings of the 2014 IEEE International Conference on Data Mining Workshops, 2014

Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

Byte The Bullet: Learning on Real-World Computing Architectures.
Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

Learning with few bits on small-scale devices: From regularization to energy efficiency.
Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

A Learning Analytics Methodology to Profile Students Behavior and Explore Interactions with a Digital Electronics Simulator.
Proceedings of the Open Learning and Teaching in Educational Communities, 2014

2013
An improved analysis of the Rademacher data-dependent bound using its self bounding property.
Neural Networks, 2013

Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic.
J. Univers. Comput. Sci., 2013

A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Some results about the Vapnik-Chervonenkis entropy and the rademacher complexity.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2013, 2013

Training Computationally Efficient Smartphone-Based Human Activity Recognition Models.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2013, 2013

A Learning Machine with a Bit-Based Hypothesis Space.
Proceedings of the 21st European Symposium on Artificial Neural Networks, 2013

A Public Domain Dataset for Human Activity Recognition using Smartphones.
Proceedings of the 21st European Symposium on Artificial Neural Networks, 2013

2012
Human Activity Recognition Using Smartphones.
Dataset, December, 2012

In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines.
IEEE Trans. Neural Networks Learn. Syst., 2012

In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine.
Neural Process. Lett., 2012

Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine.
Proceedings of the Ambient Assisted Living and Home Care - 4th International Workshop, 2012

Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Nested Sequential Minimal Optimization for Support Vector Machines.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Structural Risk Minimization and Rademacher Complexity for Regression.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

The 'K' in K-fold Cross Validation.
Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers.
Proceedings of the 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, 2011

Selecting the hypothesis space for improving the generalization ability of Support Vector Machines.
Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

In-sample model selection for Support Vector Machines.
Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

Maximal Discrepancy vs. Rademacher Complexity for error estimation.
Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

2010
Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory.
Proceedings of the International Joint Conference on Neural Networks, 2010


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