IRIS
RIDELLA, SANDRO
 Distribuzione geografica
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Continente #
EU - Europa 13.316
AS - Asia 56
SA - Sud America 26
NA - Nord America 2
AF - Africa 1
Totale 13.401
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Nazione #
IT - Italia 13.314
CN - Cina 36
BR - Brasile 21
VN - Vietnam 15
SG - Singapore 4
AR - Argentina 2
AL - Albania 1
EC - Ecuador 1
EE - Estonia 1
HN - Honduras 1
MX - Messico 1
PE - Perù 1
PK - Pakistan 1
PY - Paraguay 1
ZA - Sudafrica 1
Totale 13.401
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Città #
Genova 8.635
Genoa 2.709
Rapallo 1.124
Vado Ligure 825
Bordighera 21
Beijing 14
Ho Chi Minh City 6
Hanoi 3
Apatzingán 1
Arcos 1
Asunción 1
Belo Horizonte 1
Biên Hòa 1
Buenos Aires 1
Campinas 1
Da Nang 1
Diadema 1
Dracena 1
Duque de Caxias 1
Embu das Artes 1
Francisco Morato 1
Garanhuns 1
Guayaquil 1
Hải Dương 1
Ibirité 1
Itajaí 1
Johannesburg 1
Juiz de Fora 1
Karachi 1
Lima 1
Ninh Bình 1
Panambi 1
Paranavaí 1
Petrolina 1
Porteña 1
Porto Feliz 1
Propriá 1
Rio de Janeiro 1
Salvador 1
Singapore 1
São José 1
São Paulo 1
Sóc Trăng 1
Tallinn 1
Tegucigalpa 1
Tirana 1
Totale 13.375
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Nome #
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels 187
A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers 171
A support vector machine with integer parameters 167
A New Method for Multiclass Support Vector Machines 167
K-Winner Machines for pattern classification 162
A FPGA Core Generator for Embedded Classification Systems 161
K-Fold Generalization Capability Assessment for Support Vector Classifiers 158
Representation and generalization properties of class-entropy networks 157
Block Distortion Assessment for Image Compression Through ANNs 157
Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates 157
Automatic Hyperparameter Tuning for Support Vector Machines 156
Fast convergence of extended Rademacher Complexity bounds 156
CBP networks as a generalized neural model 154
A Hardware-friendly Support Vector Machine for Embedded Automotive Applications 153
Theoretical and Practical Model Selection Methods for Support Vector Classifiers 152
An Improved Analysis of the Rademacher Data-dependent Bound Using Its Self-Bounding Property 151
Characterization of a cellular Array of Dipoles for Molecular Information Processing 150
A local Vapnik-Chervonenkis complexity 146
A VLSI friendly algorithm for support vector machines 145
Using Unsupervised Analysis to Constrain Generalization Bounds for Support Vector Classifiers 143
A digital architecture for support vector machines: theory, algorithm and FPGA implementation 142
Tikhonov, Ivanov and Morozov regularization for support vector machine learning 142
A Support Vector Machine Classifier from a Bit-Constrained, Sparse and Localized Hypothesis Space 141
Distributed key-generation structures for associative image-classification. 141
Augmenting vector quantization with interval arithmetics for image-coding applications 141
Selecting the hypothesis space for improving the generalization ability of support vector machines 139
A model-selection approach to the VLSI design of vector quantizers 138
Some considerations about the frequency dependence of the characteristic impedance of uniform microstrips 137
Unlabeled Patterns to Tighten Rademacher Complexity Error Bounds for Kernel Classifiers 137
Computation of the scattering matrix of a class of reciprocal lossless 2-ports from one short-circuit measurement 136
In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines 136
Randomized learning: Generalization performance of old and new theoretically grounded algorithms 135
Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters 134
Performance characterization of K-Winner Machines 132
Vector Quantization Complexity and Quantum Computing 131
The k-winner machine model 131
Circuital implementation of support vector machines 130
On the importance of sorting in “Neural Gas” for training vector quantizers 130
A deep connection between the vapnik-chervonenkis entropy and the rademacher complexity 130
Maximal Discrepancy for Support Vector Machines 127
Learning the mean: A neural network approach 127
Maximal Discrepancy for Support Vector Machines 127
Incorporating a-priori knowledge into neural networks 127
Training support vector machines: a quantum-computing perspective. 126
A Learning Machine with a Bit-Based Hypothesis Space 126
Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency 126
Learning Hardware-Friendly Classifiers through Algorithmic Stability 125
Circular backpropagation networks embed vector quantization 124
Hyperparameter design criteria for support vector classifiers 124
Learning Algorithm for Nonlinear Support Vector Machines Suited for Digital VLSI 124
Adaptive representation properties of the circular back-propgation model 123
Using chaos to generate keys for associative noise-like coding memories 120
Feed-forward Support Vector Machine without Multipliers 119
Model Selection for Support Vector Machines: Advantages and Disadvantages of the Machine Learning Theory 119
Worst case analysis of weight inaccuracy effects in multilayer perceptrons 119
Adaptive internal representation in circular back-propagation networks 118
Circular back-propagation networks for classification 118
Some Results About the Vapnik-Chervonenkis Entropy and the Rademacher Complexity 118
A circuit model for terminated microstrips 116
Unsupervised Clustering and the Capacity of Support Vector Machines 116
Rademacher Complexity and Structural Risk Minimization: an Application to Human Gene Expression Datasets 116
Differential privacy and generalization: Sharper bounds with applications 115
Class-entropy minimization networks for domain analysis and rule extraction 114
Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples 114
In-Sample Model Selection for Trimmed Hinge Loss Support Vector Machine 114
Digital implementation of Hierarchical Vector Quantization 113
The ‘K’ in K-fold Cross Validation 111
Using K-Winner Machines for domain analysis 111
Fully Empirical and Data-Dependent Stability-Based Bounds 111
Testing the Augmented Binary Multiclass SVM on Microarray Data 111
Impatt diode modelling and identification 110
Test Error Bounds for Classifiers: A Survey of Old and New Results 110
Pruning and rule extraction using class entropy 110
Open-circuited coaxial lines as standards for microwave measurements 109
Comments on Microstrip Characteristic Impedance 109
Using Variable Neighborhood Search to Improve the Support Vector Machine Performance in Embedded Automotive Applications 109
Model Selection in Top Quark Tagging with a Support Vector Classifier 108
PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis 108
Generalization-based approach to plastic vector quantization 106
Electrical modeling of cells 106
Quantum optimization for training support vector machines 105
Plastic algorithm for adaptive vector quantization 103
Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities 103
Prospects of quantum-classical optimization for digital design 103
Circuit implementation of the k-winner machine 99
In-sample Model Selection for Support Vector Machines 99
Identification of a classs of two-port networks from one short-circuit measurement 98
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers. 98
K–Fold Cross Validation for Error Rate Estimate in Support Vector Machines 97
Shrinkage learning to improve SVM with hints 97
On the design of a travelling wave-thin film amplifier 96
Quantum computing and supervised machine learning: Training, model selection, and error estimation 96
Empirical Measure of Multiclass Generalization Performance: the K-Winner Machine Case 94
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap 94
Out-of-Sample Error Estimation: The Blessing of High Dimensionality 94
Local Rademacher Complexity Machine 94
Learning the appropriate representation paradigm by circular processing units 92
Quantum-computing optimization for K-Winner Machines 89
Nested Sequential Minimal Optimization for Support Vector Machine 83
Structural Risk Minimization and Rademacher Complexity for Regression 80
Totale 12.431
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Categoria #
all - tutte 41.735
article - articoli 20.828
book - libri 0
conference - conferenze 18.817
curatela - curatele 0
other - altro 0
patent - brevetti 518
selected - selezionate 0
volume - volumi 1.572
Totale 83.470
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Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2020/2021528 0 0 0 48 41 88 21 72 65 83 52 58
2021/20221.395 26 100 113 168 31 93 88 329 68 127 82 170
2022/20231.363 131 87 7 158 210 244 14 84 213 4 188 23
2023/2024633 32 101 19 57 28 118 49 33 30 16 46 104
2024/20251.821 44 185 50 99 308 194 191 239 58 66 163 224
2025/2026970 346 91 221 312 0 0 0 0 0 0 0 0
Totale 13.571