Davide Anguita

Orcid: 0000-0001-7523-5291

According to our database1, Davide Anguita authored at least 171 papers between 1991 and 2024.

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Bibliography

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

Investigating over-parameterized randomized graph networks.
Neurocomputing, 2024

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

2023
New Advances in Artificial Neural Networks and Machine Learning Techniques.
Neural Process. Lett., October, 2023

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

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

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

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

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

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

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

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

The Benefits of Adversarial Defence in Generalisation.
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

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
Spectral Analysis of Electricity Demand Using Hilbert-Huang Transform.
Sensors, 2020

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

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

Bridging Cognitive Models and Recommender Systems.
Cogn. Comput., 2020

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

2019
Local Rademacher Complexity Machine.
Neurocomputing, 2019

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

Introduction.
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

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

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

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

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

Support Vector Motion Clustering.
IEEE Trans. Circuits Syst. Video Technol., 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

Learning Analytics for a Puzzle Game to Discover the Puzzle-Solving Tactics of Players.
Proceedings of the Adaptive and Adaptable Learning, 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

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
Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression.
IEEE Trans. Smart Grid, 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 Survey of old and New Results for the Test Error Estimation of a Classifier.
J. Artif. Intell. Soft Comput. Res., 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

Human Activity and Motion Disorder Recognition: towards smarter Interactive Cognitive Environments.
Proceedings of the 21st European Symposium on Artificial Neural Networks, 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
A FPGA Core Generator for Embedded Classification Systems.
J. Circuits Syst. Comput., 2011

Maximal Discrepancy for Support Vector Machines.
Neurocomputing, 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

Test error bounds for classifiers: A survey of old and new results.
Proceedings of the IEEE Symposium on Foundations of Computational Intelligence, 2011

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

2010
Using unsupervised analysis to constrain generalization bounds for support vector classifiers.
IEEE Trans. Neural Networks, 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

2009
Nature-inspired learning and adaptive systems.
Nat. Comput., 2009

K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines.
Proceedings of The 2009 International Conference on Data Mining, 2009

2008
Support vector machines for interval discriminant analysis.
Neurocomputing, 2008

A support vector machine with integer parameters.
Neurocomputing, 2008

Using Variable Neighborhood Search to improve the Support Vector Machine performance in embedded automotive applications.
Proceedings of the International Joint Conference on Neural Networks, 2008

Smart Plankton: a Nature Inspired Underwater Wireless Sensor Network.
Proceedings of the Fourth International Conference on Natural Computation, 2008

Smart plankton - a new generation of underwater wireless sensor network.
Proceedings of the Eleventh International Conference on the Synthesis and Simulation of Living Systems, 2008

2007
A Hardware-friendly Support Vector Machine for Embedded Automotive Applications.
Proceedings of the International Joint Conference on Neural Networks, 2007

Interval discriminant analysis using support vector machines.
Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

A learning machine for resource-limited adaptive hardware.
Proceedings of the Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007), 2007

2006
Feed-Forward Support Vector Machine Without Multipliers.
IEEE Trans. Neural Networks, 2006

Nature Inspiration for Support Vector Machines.
Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems, 2006

Testing the Augmented Binary Multiclass SVM on Microarray Data.
Proceedings of the International Joint Conference on Neural Networks, 2006

2005
Data Mining Tools: From Web to Grid Architectures.
Proceedings of the Advances in Grid Computing, 2005

2004
Special issue on hardware implementations of soft computing techniques.
Appl. Soft Comput., 2004

An Algorithm for Reducing the Number of Support Vectors.
Proceedings of the Biological and Artificial Intelligence Environments, 2004

2003
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation.
IEEE Trans. Neural Networks, 2003

Digital Least Squares Support Vector Machines.
Neural Process. Lett., 2003

Quantum optimization for training support vector machines.
Neural Networks, 2003

Hyperparameter design criteria for support vector classifiers.
Neurocomputing, 2003

Neural network learning for analog VLSI implementations of support vector machines: a survey.
Neurocomputing, 2003

2002
Improved neural network for SVM learning.
IEEE Trans. Neural Networks, 2002

SVM performance assessment for the control of injection moulding processes and plasticating extrusion.
Int. J. Syst. Sci., 2002

Automatic Hyperparameter Tuning for Support Vector Machines.
Proceedings of the Artificial Neural Networks, 2002

MDL Based Model Selection for Relevance Vector Regression.
Proceedings of the Artificial Neural Networks, 2002

2001
Perspectives on dedicated hardware implementations.
Proceedings of the 9th European Symposium on Artificial Neural Networks, 2001

2000
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap.
Neural Process. Lett., 2000

A case study of a distributed high-performance computing system for neurocomputing.
J. Syst. Archit., 2000

Digital VLSI Algorithms and Architectures for Support Vector Machines.
Int. J. Neural Syst., 2000

Fast Training of Support Vector Machines for Regression.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

1999
Worst case analysis of weight inaccuracy effects in multilayer perceptrons.
IEEE Trans. Neural Networks, 1999

A VLSI friendly algorithm for support vector machines.
Proceedings of the International Joint Conference Neural Networks, 1999

Support Vector Machines: A Comparison of Some Kernel Functions.
Proceedings of the Third ICSC Symposia on Intelligent Industrial Automation (IIA'99) and Soft Computing (SOCO'99), 1999

1998
High Performance Neurocomputing: Industrial and Medical Applications of the RAIN System.
Proceedings of the High-Performance Computing and Networking, 1998

1997
RAIN: Redundant Array of Inexpensive workstations for Neurocomputing.
Proceedings of the Euro-Par '97 Parallel Processing, 1997

1996
Mixing floating- and fixed-point formats for neural network learning on neuroprocessors.
Microprocess. Microprogramming, 1996

Limiting the effects of weight errors in feedforward networks using interval arithmetic.
Proceedings of International Conference on Neural Networks (ICNN'96), 1996

1995
Neural structures for visual motion tracking.
Mach. Vis. Appl., 1995

A heterogeneous and reconfigurable machine-vision system.
Mach. Vis. Appl., 1995

Object Oriented Design of a Simulator for Large BP Neural Networks.
Proceedings of the From Natural to Artificial Neural Computation, 1995

Learning in large neural networks.
Proceedings of the High-Performance Computing and Networking, 1995

1994
Associative structures for vision.
Multidimens. Syst. Signal Process., 1994

An efficient implementation of BP on RISC-based workstations.
Neurocomputing, 1994

1991
Transputer-based architectures for associative image classification.
Proceedings of the Third IEEE Symposium on Parallel and Distributed Processing, 1991


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