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TNN
2010

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Novel maximum-margin training algorithms for supervised neural networks

12 years 11 months ago

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This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...

Oswaldo Ludwig, Urbano Nunes

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Id	Views	Title	Status

1	234	Novel maximum-margin training algorithms for supervised neural networks	claim
2	233	A hierarchical RBF online learning algorithm for real-time 3-D scanner	claim
3	227	Self-Organizing MultiLayer Perceptron	claim
4	223	Linear Discriminant Analysis for Signatures	claim
5	216	Simplifying mixture models through function approximation	claim
6	205	Behavior-constrained support vector machines for fMRI data analysis	claim
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8	182	A discrete-time neural network for optimization problems with hybrid constraints	claim
9	182	A contrast function for independent component analysis without permutation ambiguity	claim
10	176	Sparse approximation through boosting for learning large scale kernel machines	claim
11	176	On the weight convergence of Elman networks	claim
12	174	Equivalences between neural-autoregressive time series models and fuzzy systems	claim
13	173	Global synchronization for discrete-time stochastic complex networks with randomly occurred nonlinearities and mixed time delays	claim
14	173	Multiclass relevance vector machines: sparsity and accuracy	claim
15	171	Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks	claim
16	168	On the selection of weight decay parameter for faulty networks	claim
17	159	Multiple incremental decremental learning of support vector machines	claim
18	155	Incorporating the loss function into discriminative clustering of structured outputs	claim
19	154	Discriminative semi-supervised feature selection via manifold regularization	claim
20	149	A rank-one update algorithm for fast solving kernel Foley-Sammon optimal discriminant vectors	claim
21	148	A fast algorithm for robust mixtures in the presence of measurement errors	claim
22	148	Generalized low-rank approximations of matrices revisited	claim
23	147	Theoretical Model for Mesoscopic-Level Scale-Free Self-Organization of Functional Brain Networks	claim
24	147	State-space analysis of Boolean networks	claim
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28	132	IPADE: Iterative Prototype Adjustment for Nearest Neighbor Classification	claim
29	130	Large developing receptive fields using a distributed and locally reprogrammable address-event receiver	claim
30	127	RAMOBoost: ranked minority oversampling in boosting	claim
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33	115	New Approach for the Identification and Validation of a Nonlinear F/A-18 Model by Use of Neural Networks	claim

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