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Sandro Ridella
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2020 – today
- 2024
- [j58]Luca Oneto
, Sandro Ridella, Davide Anguita
Towards algorithms and models that we can trust: A theoretical perspective. Neurocomputing 592: 127798 (2024) - 2023
- [j57]Luca Oneto
Do we really need a new theory to understand over-parameterization? Neurocomputing 543: 126227 (2023) - [c46]Luca Oneto, Sandro Ridella, Davide Anguita:
Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective. ESANN 2023 - 2022
- [j56]Luca Oneto
The benefits of adversarial defense in generalization. Neurocomputing 505: 125-141 (2022) - [c45]Luca Oneto, Sandro Ridella, Davide Anguita:
Do We Really Need a New Theory to Understand the Double-Descent? ESANN 2022 - 2021
- [j55]Luca Oneto
Distribution-Dependent Weighted Union Bound. Entropy 23(1): 101 (2021) - [c44]Luca Oneto
The Benefits of Adversarial Defence in Generalisation. ESANN 2021 - 2020
- [c43]Luca Oneto, Sandro Ridella, Davide Anguita:
Improving the Union Bound: a Distribution Dependent Approach. ESANN 2020: 423-428
2010 – 2019
- 2019
- [j54]Luca Oneto
Local Rademacher Complexity Machine. Neurocomputing 342: 24-32 (2019) - 2018
- [j53]Luca Oneto
Randomized learning: Generalization performance of old and new theoretically grounded algorithms. Neurocomputing 298: 21-33 (2018) - [j52]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) - [c42]Luca Oneto, Sandro Ridella, Davide Anguita:
Local Rademacher Complexity Machine. ESANN 2018 - 2017
- [j51]Luca Oneto
Differential privacy and generalization: Sharper bounds with applications. Pattern Recognit. Lett. 89: 31-38 (2017) - [c41]Luca Oneto, Sandro Ridella, Davide Anguita:
Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions. ESANN 2017 - 2016
- [j50]Luca Oneto
Tikhonov, Ivanov and Morozov regularization for support vector machine learning. Mach. Learn. 103(1): 103-136 (2016) - [j49]Luca Oneto
A local Vapnik-Chervonenkis complexity. Neural Networks 82: 62-75 (2016) - [j48]Luca Oneto
Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates. Neural Process. Lett. 43(2): 567-602 (2016) - [j47]Luca Oneto
PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis. Pattern Recognit. Lett. 80: 200-207 (2016) - [j46]Luca Oneto
Learning Hardware-Friendly Classifiers Through Algorithmic Stability. ACM Trans. Embed. Comput. Syst. 15(2): 23:1-23:29 (2016) - [c40]Luca Oneto, Sandro Ridella, Davide Anguita:
Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data. ESANN 2016 - 2015
- [j45]Luca Oneto
Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency. Neurocomputing 169: 225-235 (2015) - [j44]Luca Oneto
Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples. Neural Networks 65: 115-125 (2015) - [j43]Luca Oneto
Fully Empirical and Data-Dependent Stability-Based Bounds. IEEE Trans. Cybern. 45(9): 1913-1926 (2015) - [c39]Luca Oneto
Shrinkage learning to improve SVM with hints. IJCNN 2015: 1-9 - [c38]Luca Oneto
Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters. IJCNN 2015: 1-4 - [c37]Luca Oneto
Fast convergence of extended Rademacher Complexity bounds. IJCNN 2015: 1-10 - 2014
- [j42]Davide Anguita
Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers. Pattern Recognit. Lett. 37: 210-219 (2014) - [j41]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) - [c36]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
Learning with few bits on small-scale devices: From regularization to energy efficiency. ESANN 2014 - [c35]Luca Oneto
Out-of-Sample Error Estimation: The Blessing of High Dimensionality. ICDM Workshops 2014: 637-644 - [c34]Davide Anguita
Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities. IJCNN 2014: 3916-3921 - 2013
- [j40]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) - [j39]Luca Oneto
An improved analysis of the Rademacher data-dependent bound using its self bounding property. Neural Networks 44: 107-111 (2013) - [c33]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
A Learning Machine with a Bit-Based Hypothesis Space. ESANN 2013 - [c32]Davide Anguita
A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers. ICANN 2013: 434-441 - [c31]Davide Anguita
Some results about the Vapnik-Chervonenkis entropy and the rademacher complexity. IJCNN 2013: 1-8 - [c30]Davide Anguita
A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space. IJCNN 2013: 1-10 - 2012
- [j38]Sergio Decherchi
Learning the mean: A neural network approach. Neurocomputing 77(1): 129-143 (2012) - [j37]Davide Anguita
In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine. Neural Process. Lett. 36(3): 275-283 (2012) - [j36]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) - [c29]Davide Anguita, Luca Ghelardoni, Alessandro Ghio, Luca Oneto, Sandro Ridella:
The 'K' in K-fold Cross Validation. ESANN 2012 - [c28]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
Structural Risk Minimization and Rademacher Complexity for Regression. ESANN 2012 - [c27]Alessandro Ghio, Davide Anguita
Nested Sequential Minimal Optimization for Support Vector Machines. ICANN (2) 2012: 156-163 - [c26]Luca Oneto
Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets. ICANN (2) 2012: 491-498 - 2011
- [j35]Davide Anguita
Maximal Discrepancy for Support Vector Machines. Neurocomputing 74(9): 1436-1443 (2011) - [j34]Davide Anguita
A FPGA Core Generator for Embedded Classification Systems. J. Circuits Syst. Comput. 20(2): 263-282 (2011) - [c25]Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
Maximal Discrepancy vs. Rademacher Complexity for error estimation. ESANN 2011 - [c24]Davide Anguita
Test error bounds for classifiers: A survey of old and new results. FOCI 2011: 80-87 - [c23]Davide Anguita
In-sample model selection for Support Vector Machines. IJCNN 2011: 1154-1161 - [c22]Davide Anguita
Selecting the hypothesis space for improving the generalization ability of Support Vector Machines. IJCNN 2011: 1169-1176 - [c21]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
- [j33]Sergio Decherchi
Using unsupervised analysis to constrain generalization bounds for support vector classifiers. IEEE Trans. Neural Networks 21(3): 424-438 (2010) - [c20]Davide Anguita, Alessandro Ghio, Sandro Ridella:
Maximal Discrepancy for Support Vector Machines. ESANN 2010 - [c19]Davide Anguita
Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory. IJCNN 2010: 1-8 - [c18]Sergio Decherchi
A neural model approach for regularization in the mean estimation case. IJCNN 2010: 1-7
2000 – 2009
- 2009
- [c17]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
- [j32]Davide Anguita
A support vector machine with integer parameters. Neurocomputing 72(1-3): 480-489 (2008) - [c16]Enrique Alba
Using Variable Neighborhood Search to improve the Support Vector Machine performance in embedded automotive applications. IJCNN 2008: 984-988 - 2007
- [c15]Davide Anguita
A Hardware-friendly Support Vector Machine for Embedded Automotive Applications. IJCNN 2007: 1360-1364 - 2006
- [j31]Paolo Gastaldo, Sandro Ridella
Prospects of quantum-classical optimization for digital design. Appl. Math. Comput. 179(2): 581-595 (2006) - [j30]Davide Anguita
Feed-Forward Support Vector Machine Without Multipliers. IEEE Trans. Neural Networks 17(5): 1328-1331 (2006) - [c14]Davide Anguita
Testing the Augmented Binary Multiclass SVM on Microarray Data. IJCNN 2006: 1966-1968 - 2004
- [j29]Sandro Ridella
Using K-Winner Machines for domain analysis. Neurocomputing 62: 367-388 (2004) - [c13]Davide Anguita, Sandro Ridella, Fabio Rivieccio:
An Algorithm for Reducing the Number of Support Vectors. WIRN 2004: 99-105 - 2003
- [j28]Davide Anguita
Hyperparameter design criteria for support vector classifiers. Neurocomputing 55(1-2): 109-134 (2003) - [j27]Davide Anguita
Quantum optimization for training support vector machines. Neural Networks 16(5-6): 763-770 (2003) - [j26]Davide Anguita
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation. IEEE Trans. Neural Networks 14(5): 993-1009 (2003) - [j25]Massimiliano Bracco, Sandro Ridella
Digital implementation of hierarchical vector quantization. IEEE Trans. Neural Networks 14(5): 1072-1084 (2003) - 2002
- [c12]Davide Anguita, Sandro Ridella, Fabio Rivieccio, Rodolfo Zunino:
Automatic Hyperparameter Tuning for Support Vector Machines. ICANN 2002: 1345-1350 - 2001
- [j24]Sandro Ridella
K-winner machines for pattern classification. IEEE Trans. Neural Networks 12(2): 371-385 (2001) - [j23]Sandro Ridella
Empirical measure of multiclass generalization performance: the K-winner machine case. IEEE Trans. Neural Networks 12(6): 1525-1529 (2001) - 2000
- [j22]Davide Anguita
Digital VLSI Algorithms and Architectures for Support Vector Machines. Int. J. Neural Syst. 10(3): 159-170 (2000) - [j21]Davide Anguita
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap. Neural Process. Lett. 11(1): 51-58 (2000) - [c11]Sandro Ridella, Stefano Rovetta, Rodolfo Zunino:
The K-Winner Machine Model. IJCNN (1) 2000: 106-113 - [c10]Sandro Ridella, Stefano Rovetta, Rodolfo Zunino:
Augmenting vector quantization with interval arithmetics for image-coding applications. ISCAS 2000: 307-310
1990 – 1999
- 1999
- [j20]Gian Paolo Drago, Sandro Ridella:
Possibility and Necessity Pattern Classification using an Interval Arithmetic Perceptron. Neural Comput. Appl. 8(1): 40-52 (1999) - [j19]Sandro Ridella
Representation and generalization properties of class-entropy networks. IEEE Trans. Neural Networks 10(1): 31-47 (1999) - [j18]Davide Anguita
Worst case analysis of weight inaccuracy effects in multilayer perceptrons. IEEE Trans. Neural Networks 10(2): 415-418 (1999) - [j17]Sandro Ridella
Circular backpropagation networks embed vector quantization. IEEE Trans. Neural Networks 10(4): 972-975 (1999) - [c9]Davide Anguita, Andrea Boni, Sandro Ridella:
Support Vector Machines: A Comparison of Some Kernel Functions. IIA/SOCO 1999 - [c8]Davide Anguita
A VLSI friendly algorithm for support vector machines. IJCNN 1999: 939-942 - 1998
- [j16]Gian Paolo Drago, Sandro Ridella
Pruning with interval arithmetic perceptron. Neurocomputing 18(1-3): 229-246 (1998) - [j15]Sandro Ridella, Stefano Rovetta, Rodolfo Zunino:
Plastic Algorithm for Adaptive Vector Quantisation. Neural Comput. Appl. 7(1): 37-51 (1998) - 1997
- [j14]Sandro Ridella, Stefano Rovetta, Rodolfo Zunino:
Circular backpropagation networks for classification. IEEE Trans. Neural Networks 8(1): 84-97 (1997) - [c7]Sandro Ridella
CBP networks as a generalized neural model. ICNN 1997: 210-214 - [c6]Fabio Ancona, Sandro Ridella, Stefano Rovetta
On the importance of sorting in "neural gas" training of vector quantizers. ICNN 1997: 1804-1808 - 1996
- [j13]Gian Paolo Drago, Sandro Ridella
On the convergence of a growing topology neural algorithm. Neurocomputing 12(2-3): 171-185 (1996) - [c5]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
- [j12]Gian Paolo Drago, Mauro Morando, Sandro Ridella
An Adaptive Momentum Back Propagation (AMBP). Neural Comput. Appl. 3(4): 213-221 (1995) - [j11]Sandro Ridella
Adaptive Internal Representation in Circular Back-Propagation Networks. Neural Comput. Appl. 3(4): 222-333 (1995) - [c4]Sandro Ridella, Stefano Rovetta, Rodolfo Zunino:
Learning the appropriate representation paradigm by circular processing units. ESANN 1995 - 1994
- [j10]Sandro Ridella
Class-Entropy Minimisation Networks for Domain Analysis and Rule Extraction. Neural Comput. Appl. 2(1): 40-52 (1994) - [j9]Gian Paolo Drago, Sandro Ridella
Convergence Properties of Cascade Correlation in Function Approximation. Neural Comput. Appl. 2(3): 142-147 (1994) - 1993
- [j8]Giancarlo Parodi, Sandro Ridella
Using chaos to generate keys for associative noise-like coding memories. Neural Networks 6(4): 559-572 (1993) - [c3]Sandro Ridella
Pruning and rule extraction using class entropy. ICNN 1993: 250-256 - 1992
- [j7]Marco Muselli, Sandro Ridella:
Global Optimization of Functions with the Interval Genetic Algorithm. Complex Syst. 6(3) (1992) - [j6]Alessandro De Gloria, Paolo Faraboschi
A dedicated massively parallel architecture for the Boltzmann machine. Parallel Comput. 18(1): 57-73 (1992) - [j5]Gian Paolo Drago, Sandro Ridella
Statistically controlled activation weight initialization (SCAWI). IEEE Trans. Neural Networks 3(4): 627-631 (1992) - 1991
- [j4]Angelo Corana
Efficient computation of the correlation dimension from a time series on a LIW computer. Parallel Comput. 17(6-7): 809-820 (1991)
1980 – 1989
- 1989
- [j3]Angelo Corana
Corrigenda: "Minimizing Multimodal Functions of Continuous Variables with the 'Simulated Annealing' Algorithm". ACM Trans. Math. Softw. 15(3): 287 (1989) - 1988
- [j2]A. Corona, Claudio Martini, Mauro Morando, Sandro Ridella
Solving linear equation systems on vector computers with maximum efficiency. Parallel Comput. 8(1-3): 133-139 (1988) - 1987
- [j1]Angelo Corana
Minimizing multimodal functions of continuous variables with the "simulated annealing" algorithm. ACM Trans. Math. Softw. 13(3): 262-280 (1987) - [c2]Angelo Corana
LU Factorization with Maximum Performances on FPS Architectures 38/64 BIT. ICS 1987: 782-788 - 1986
- [c1]Claudio Martini, Mauro Morando, Sandro Ridella:
Caltech Hypercube MIMD Computer Performances Measurements in a Physical Mathematical Application. CONPAR 1986: 128-132
Coauthor Index
[j58] [j57] [c46] [j56] [c45] [c44] [c43] [j54] [j53] [j52] [c42] [j51] [c41] [j50] [j49] [j48] [j47] [j46] [c40] [j45] [j44] [j43] [c39] [c38] [c37] [j42] [j41] [c36] [c35] [c34] [j40] [j39] [c33] [c32] [c31] [c30] [j37] [j36] [c29] [c28] [c27] [c26] [j35] [j34] [c25] [c24] [c23] [c22] [c21] [j33] [c20] [c19] [c17] [j32] [c16] [c15] [j30] [c14] [c13] [j28] [j27] [j26] [c12] [j22] [j21] [j18] [c9] [c8] [c5]
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last updated on 2024-06-19 21:53 CEST by the dblp team
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