IRIS
RIDELLA, SANDRO
 Distribuzione geografica
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Continente #
EU - Europa 10.657
Totale 10.657
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Nazione #
IT - Italia 10.657
Totale 10.657
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Città #
Genova 8.635
Rapallo 1.124
Genoa 857
Bordighera 21
Vado Ligure 20
Totale 10.657
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Nome #
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels 156
Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates 136
Representation and generalization properties of class-entropy networks 134
A support vector machine with integer parameters 133
K-Winner Machines for pattern classification 130
CBP networks as a generalized neural model 129
Theoretical and Practical Model Selection Methods for Support Vector Classifiers 129
Block Distortion Assessment for Image Compression Through ANNs 128
Automatic Hyperparameter Tuning for Support Vector Machines 127
K-Fold Generalization Capability Assessment for Support Vector Classifiers 127
A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers 126
Distributed key-generation structures for associative image-classification. 126
Selecting the hypothesis space for improving the generalization ability of support vector machines 125
A New Method for Multiclass Support Vector Machines 124
Tikhonov, Ivanov and Morozov regularization for support vector machine learning 121
A FPGA Core Generator for Embedded Classification Systems 120
A VLSI friendly algorithm for support vector machines 120
Using Unsupervised Analysis to Constrain Generalization Bounds for Support Vector Classifiers 119
Some considerations about the frequency dependence of the characteristic impedance of uniform microstrips 119
Fast convergence of extended Rademacher Complexity bounds 119
Characterization of a cellular Array of Dipoles for Molecular Information Processing 118
Unlabeled Patterns to Tighten Rademacher Complexity Error Bounds for Kernel Classifiers 118
In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines 117
Performance characterization of K-Winner Machines 116
Vector Quantization Complexity and Quantum Computing 115
An Improved Analysis of the Rademacher Data-dependent Bound Using Its Self-Bounding Property 112
Maximal Discrepancy for Support Vector Machines 112
A Hardware-friendly Support Vector Machine for Embedded Automotive Applications 112
Maximal Discrepancy for Support Vector Machines 111
Circular backpropagation networks embed vector quantization 110
Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency 110
Learning Algorithm for Nonlinear Support Vector Machines Suited for Digital VLSI 109
Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters 109
Computation of the scattering matrix of a class of reciprocal lossless 2-ports from one short-circuit measurement 108
Hyperparameter design criteria for support vector classifiers 107
Circuital implementation of support vector machines 107
A model-selection approach to the VLSI design of vector quantizers 107
Some Results About the Vapnik-Chervonenkis Entropy and the Rademacher Complexity 106
Learning the mean: A neural network approach 106
A digital architecture for support vector machines: theory, algorithm and FPGA implementation 105
Training support vector machines: a quantum-computing perspective. 105
Learning Hardware-Friendly Classifiers through Algorithmic Stability 105
Augmenting vector quantization with interval arithmetics for image-coding applications 105
A Support Vector Machine Classifier from a Bit-Constrained, Sparse and Localized Hypothesis Space 104
Incorporating a-priori knowledge into neural networks 103
Feed-forward Support Vector Machine without Multipliers 101
Using chaos to generate keys for associative noise-like coding memories 101
The k-winner machine model 101
Using K-Winner Machines for domain analysis 101
Digital implementation of Hierarchical Vector Quantization 101
Model Selection for Support Vector Machines: Advantages and Disadvantages of the Machine Learning Theory 100
Worst case analysis of weight inaccuracy effects in multilayer perceptrons 100
In-Sample Model Selection for Trimmed Hinge Loss Support Vector Machine 100
Rademacher Complexity and Structural Risk Minimization: an Application to Human Gene Expression Datasets 99
A local Vapnik-Chervonenkis complexity 99
Circular back-propagation networks for classification 98
Adaptive representation properties of the circular back-propgation model 97
Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples 97
The ‘K’ in K-fold Cross Validation 96
Differential privacy and generalization: Sharper bounds with applications 96
Quantum optimization for training support vector machines 94
Test Error Bounds for Classifiers: A Survey of Old and New Results 94
Randomized learning: Generalization performance of old and new theoretically grounded algorithms 94
Adaptive internal representation in circular back-propagation networks 93
Open-circuited coaxial lines as standards for microwave measurements 93
Model Selection in Top Quark Tagging with a Support Vector Classifier 93
Prospects of quantum-classical optimization for digital design 90
Generalization-based approach to plastic vector quantization 89
Pruning and rule extraction using class entropy 89
Using Variable Neighborhood Search to Improve the Support Vector Machine Performance in Embedded Automotive Applications 89
On the importance of sorting in “Neural Gas” for training vector quantizers 88
A deep connection between the vapnik-chervonenkis entropy and the rademacher complexity 88
Circuit implementation of the k-winner machine 87
A Learning Machine with a Bit-Based Hypothesis Space 87
PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis 87
Testing the Augmented Binary Multiclass SVM on Microarray Data 87
Electrical modeling of cells 86
Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities 85
Fully Empirical and Data-Dependent Stability-Based Bounds 85
Comments on Microstrip Characteristic Impedance 84
Out-of-Sample Error Estimation: The Blessing of High Dimensionality 84
Empirical Measure of Multiclass Generalization Performance: the K-Winner Machine Case 83
In-sample Model Selection for Support Vector Machines 83
Plastic algorithm for adaptive vector quantization 82
K–Fold Cross Validation for Error Rate Estimate in Support Vector Machines 81
On the design of a travelling wave-thin film amplifier 81
A circuit model for terminated microstrips 80
Learning the appropriate representation paradigm by circular processing units 79
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap 79
Shrinkage learning to improve SVM with hints 79
Unsupervised Clustering and the Capacity of Support Vector Machines 78
Local Rademacher Complexity Machine 76
Class-entropy minimization networks for domain analysis and rule extraction 72
Quantum computing and supervised machine learning: Training, model selection, and error estimation 71
Impatt diode modelling and identification 70
Nested Sequential Minimal Optimization for Support Vector Machine 70
Identification of a classs of two-port networks from one short-circuit measurement 65
Non-interacting Ellipsoidal Body Suspensions 63
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers. 62
Launcher's and microstrip characterization 61
Totale 10.053
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Categoria #
all - tutte 28.049
article - articoli 14.194
book - libri 0
conference - conferenze 12.506
curatela - curatele 0
other - altro 0
patent - brevetti 293
selected - selezionate 0
volume - volumi 1.056
Totale 56.098
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Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2019/20203.125 146 77 153 191 271 318 465 194 290 521 331 168
2020/2021800 72 111 89 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
2024/202540 40 0 0 0 0 0 0 0 0 0 0 0
Totale 10.820