Follow
Sandro Ridella
Title
Cited by
Cited by
Year
Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here
A Corana, M Marchesi, C Martini, S Ridella
ACM Transactions on Mathematical Software (TOMS) 13 (3), 262-280, 1987
21271987
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation
D Anguita, A Boni, S Ridella
IEEE Transactions on neural networks 14 (5), 993-1009, 2003
2622003
Statistically controlled activation weight initialization (SCAWI)
GP Drago, S Ridella
IEEE Transactions on Neural Networks 3 (4), 627-631, 1992
1851992
Circular backpropagation networks for classification
S Ridella, S Rovetta, R Zunino
IEEE transactions on neural networks 8 (1), 84-97, 1997
1801997
The ‘K’in K-fold cross validation
D Anguita, L Ghelardoni, A Ghio, L Oneto, S Ridella
20th European Symposium on Artificial Neural Networks, Computational …, 2012
1702012
Theoretical and practical model selection methods for support vector classifiers
D Anguita, A Boni, S Ridella, F Rivieccio, D Sterpi
Support vector machines: theory and applications, 159-179, 2005
1252005
In-sample and out-of-sample model selection and error estimation for support vector machines
D Anguita, A Ghio, L Oneto, S Ridella
IEEE Transactions on Neural Networks and Learning Systems 23 (9), 1390-1406, 2012
1222012
K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines.
D Anguita, A Ghio, S Ridella, D Sterpi
DMIN, 291-297, 2009
972009
Quantum optimization for training support vector machines
D Anguita, S Ridella, F Rivieccio, R Zunino
Neural Networks 16 (5-6), 763-770, 2003
882003
Hyperparameter design criteria for support vector classifiers
D Anguita, S Ridella, F Rivieccio, R Zunino
Neurocomputing 55 (1-2), 109-134, 2003
762003
Feed-forward support vector machine without multipliers
D Anguita, S Pischiutta, S Ridella, D Sterpi
IEEE Transactions on Neural Networks 17 (5), 1328-1331, 2006
742006
Model selection for support vector machines: Advantages and disadvantages of the machine learning theory
D Anguita, A Ghio, N Greco, L Oneto, S Ridella
The 2010 international joint conference on neural networks (IJCNN), 1-8, 2010
732010
Tikhonov, Ivanov and Morozov regularization for support vector machine learning
L Oneto, S Ridella, D Anguita
Machine Learning 103 (1), 103-136, 2016
652016
A hardware-friendly support vector machine for embedded automotive applications
D Anguita, A Ghio, S Pischiutta, S Ridella
2007 international joint conference on neural networks, 1360-1364, 2007
652007
Evaluating the generalization ability of support vector machines through the bootstrap
D Anguita, A Boni, S Ridella
Neural Processing Letters 11 (1), 51-58, 2000
612000
Circuital implementation of support vector machines
D Anguita, S Ridella, S Rovetta
Electronics Letters 34 (16), 1596-1597, 1998
561998
Some considerations about the frequency dependence of the characteristic impedance of uniform microstrips
B Bianco, L Panini, M Parodi, S Ridella
IEEE Transactions on microwave theory and techniques 26 (3), 182-185, 1978
541978
Study of bound water of poly-adenine using high frequency dielectric measurements
S Takashima, A Casaleggio, F Giuliano, M Morando, P Arrigo, S Ridella
Biophysical journal 49 (5), 1003-1008, 1986
521986
Launcher and microstrip characterization
B Bianco, M Parodi, S Ridella, F Selvaggi
IEEE Transactions on Instrumentation and Measurement, 320-323, 1976
521976
A support vector machine with integer parameters
D Anguita, A Ghio, S Pischiutta, S Ridella
Neurocomputing 72 (1-3), 480-489, 2008
512008
The system can't perform the operation now. Try again later.
Articles 1–20