Sandro Ridella

According to our database1, Sandro Ridella authored at least 96 papers between 1986 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2021
Distribution-Dependent Weighted Union Bound.
Entropy, 2021

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Learning the mean: A neural network approach.
Neurocomputing, 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 ESANN 2011, 2011

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

A neural model approach for regularization in the mean estimation case.
Proceedings of the International Joint Conference on 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
K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines.
Proceedings of The 2009 International Conference on Data Mining, 2009

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

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

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

Prospects of quantum-classical optimization for digital design.
Appl. Math. Comput., 2006

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

2004
Using K-Winner Machines for domain analysis.
Neurocomputing, 2004

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

2003
Digital implementation of hierarchical vector quantization.
IEEE Trans. Neural Networks, 2003

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

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

Hyperparameter design criteria for support vector classifiers.
Neurocomputing, 2003

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

2001
Empirical measure of multiclass generalization performance: the K-winner machine case.
IEEE Trans. Neural Networks, 2001

K-winner machines for pattern classification.
IEEE Trans. Neural Networks, 2001

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

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

Augmenting vector quantization with interval arithmetics for image-coding applications.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2000

The K-Winner Machine Model.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

1999
Circular backpropagation networks embed vector quantization.
IEEE Trans. Neural Networks, 1999

Representation and generalization properties of class-entropy networks.
IEEE Trans. Neural Networks, 1999

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

Possibility and Necessity Pattern Classification using an Interval Arithmetic Perceptron.
Neural Comput. Appl., 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
Plastic Algorithm for Adaptive Vector Quantisation.
Neural Comput. Appl., 1998

Pruning with interval arithmetic perceptron.
Neurocomputing, 1998

1997
Circular backpropagation networks for classification.
IEEE Trans. Neural Networks, 1997

CBP networks as a generalized neural model.
Proceedings of International Conference on Neural Networks (ICNN'97), 1997

On the importance of sorting in "neural gas" training of vector quantizers.
Proceedings of International Conference on Neural Networks (ICNN'97), 1997

1996
On the convergence of a growing topology neural algorithm.
Neurocomputing, 1996

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

1995
Adaptive Internal Representation in Circular Back-Propagation Networks.
Neural Comput. Appl., 1995

An Adaptive Momentum Back Propagation (AMBP).
Neural Comput. Appl., 1995

Learning the appropriate representation paradigm by circular processing units.
Proceedings of the ESANN 1995, 1995

1994
Class-Entropy Minimisation Networks for Domain Analysis and Rule Extraction.
Neural Comput. Appl., 1994

Convergence Properties of Cascade Correlation in Function Approximation.
Neural Comput. Appl., 1994

1993
Using chaos to generate keys for associative noise-like coding memories.
Neural Networks, 1993

Pruning and rule extraction using class entropy.
Proceedings of International Conference on Neural Networks (ICNN'88), San Francisco, CA, USA, March 28, 1993

1992
Statistically controlled activation weight initialization (SCAWI).
IEEE Trans. Neural Networks, 1992

A dedicated massively parallel architecture for the Boltzmann machine.
Parallel Comput., 1992

Global Optimization of Functions with the Interval Genetic Algorithm.
Complex Syst., 1992

1991
Efficient computation of the correlation dimension from a time series on a LIW computer.
Parallel Comput., 1991

1989
Corrigenda: "Minimizing Multimodal Functions of Continuous Variables with the 'Simulated Annealing' Algorithm".
ACM Trans. Math. Softw., 1989

1988
Solving linear equation systems on vector computers with maximum efficiency.
Parallel Comput., 1988

1987
Minimizing multimodal functions of continuous variables with the "simulated annealing" algorithm.
ACM Trans. Math. Softw., 1987

LU Factorization with Maximum Performances on FPS Architectures 38/64 BIT.
Proceedings of the Supercomputing, 1987

1986
Caltech Hypercube MIMD Computer Performances Measurements in a Physical Mathematical Application.
Proceedings of the CONPAR 86: Conference on Algorithms and Hardware for Parallel Processing, 1986


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