| 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 | 2217 | 1987 |
| The'K'in K-fold Cross Validation. D Anguita, L Ghelardoni, A Ghio, L Oneto, S Ridella ESANN 102, 441-446, 2012 | 470 | 2012 |
| 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 | 285 | 2003 |
| Statistically controlled activation weight initialization (SCAWI) GP Drago, S Ridella IEEE Transactions on Neural Networks 3 (4), 627-631, 1992 | 197 | 1992 |
| Circular backpropagation networks for classification S Ridella, S Rovetta, R Zunino IEEE transactions on neural networks 8 (1), 84-97, 1997 | 183 | 1997 |
| 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 | 153 | 2005 |
| 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 | 143 | 2012 |
| K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines. D Anguita, A Ghio, S Ridella, D Sterpi DMIN, 291-297, 2009 | 142 | 2009 |
| 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 | 132 | 2010 |
| Quantum optimization for training support vector machines D Anguita, S Ridella, F Rivieccio, R Zunino Neural Networks 16 (5-6), 763-770, 2003 | 131 | 2003 |
| Tikhonov, Ivanov and Morozov regularization for support vector machine learning L Oneto, S Ridella, D Anguita Machine Learning 103, 103-136, 2016 | 86 | 2016 |
| Hyperparameter design criteria for support vector classifiers D Anguita, S Ridella, F Rivieccio, R Zunino Neurocomputing 55 (1-2), 109-134, 2003 | 82 | 2003 |
| 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 | 79 | 2006 |
| 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 | 68 | 2007 |
| Evaluating the generalization ability of support vector machines through the bootstrap D Anguita, A Boni, S Ridella Neural Processing Letters 11, 51-58, 2000 | 65 | 2000 |
| Circuital implementation of support vector machines D Anguita, S Ridella, S Rovetta Electronics Letters 34 (16), 1596-1597, 1998 | 59 | 1998 |
| 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 | 59 | 1978 |
| Launcher and microstrip characterization B Bianco, M Parodi, S Ridella, F Selvaggi IEEE Transactions on Instrumentation and Measurement, 320-323, 1976 | 56 | 1976 |
| A support vector machine with integer parameters D Anguita, A Ghio, S Pischiutta, S Ridella Neurocomputing 72 (1-3), 480-489, 2008 | 54 | 2008 |
| 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 | 52 | 1986 |
Davide AnguitaDIBRIS - University of Genova, ItalyEmail verificata su unige.it
