JMLR 2006 - PDF Preprint Papers

JMLR 2006

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JMLR
2006

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A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis

14 years 1 months ago

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This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers....

Enrique Castillo, Bertha Guijarro-Berdiñas,...

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Id	Views	Title	Status

1	389	A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis	claim
2	206	New Algorithms for Efficient High-Dimensional Nonparametric Classification	claim
3	190	Causal Graph Based Decomposition of Factored MDPs	claim
4	186	Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples	claim
5	175	Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming	claim
6	169	Bayesian Network Learning with Parameter Constraints	claim
7	156	Large Scale Multiple Kernel Learning	claim
8	153	Collaborative Multiagent Reinforcement Learning by Payoff Propagation	claim
9	150	Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization	claim
10	150	Building Support Vector Machines with Reduced Classifier Complexity	claim
11	148	Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis	claim
12	148	Walk-Sums and Belief Propagation in Gaussian Graphical Models	claim
13	147	Spam Filtering Based On The Analysis Of Text Information Embedded Into Images	claim
14	145	Ensemble Pruning Via Semi-definite Programming	claim
15	143	Consistency and Convergence Rates of One-Class SVMs and Related Algorithms	claim
16	143	Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation	claim
17	143	Segmental Hidden Markov Models with Random Effects for Waveform Modeling	claim
18	140	Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error	claim
19	138	Noisy-OR Component Analysis and its Application to Link Analysis	claim
20	137	Bounds for Linear Multi-Task Learning	claim
21	136	Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis	claim
22	135	Quantile Regression Forests	claim
23	135	Statistical Comparisons of Classifiers over Multiple Data Sets	claim
24	134	Considering Cost Asymmetry in Learning Classifiers	claim
25	132	Learning to Detect and Classify Malicious Executables in the Wild	claim
26	132	Accurate Error Bounds for the Eigenvalues of the Kernel Matrix	claim
27	131	On Representing and Generating Kernels by Fuzzy Equivalence Relations	claim
28	131	Incremental Support Vector Learning: Analysis, Implementation and Applications	claim
29	125	Spam Filtering Using Statistical Data Compression Models	claim
30	125	A Linear Non-Gaussian Acyclic Model for Causal Discovery	claim
31	125	Efficient Learning of Label Ranking by Soft Projections onto Polyhedra	claim
32	125	Linear Programming Relaxations and Belief Propagation - An Empirical Study	claim
33	124	A Direct Method for Building Sparse Kernel Learning Algorithms	claim
34	124	Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problem	claim
35	124	Policy Gradient in Continuous Time	claim
36	123	Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies	claim
37	120	Learning Parts-Based Representations of Data	claim
38	120	Kernel-Based Learning of Hierarchical Multilabel Classification Models	claim
39	118	Learning Factor Graphs in Polynomial Time and Sample Complexity	claim
40	117	On the Complexity of Learning Lexicographic Strategies	claim
41	116	Step Size Adaptation in Reproducing Kernel Hilbert Space	claim
42	116	Point-Based Value Iteration for Continuous POMDPs	claim
43	115	Structured Prediction, Dual Extragradient and Bregman Projections	claim
44	113	Generalized Bradley-Terry Models and Multi-Class Probability Estimates	claim
45	113	Learning the Structure of Linear Latent Variable Models	claim
46	113	On Inferring Application Protocol Behaviors in Encrypted Network Traffic	claim
47	112	Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting	claim
48	109	Some Discriminant-Based PAC Algorithms	claim
49	108	Learning Spectral Clustering, With Application To Speech Separation	claim
50	108	The Interplay of Optimization and Machine Learning Research	claim
51	107	Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation	claim
52	107	Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss	claim
53	106	Stability Properties of Empirical Risk Minimization over Donsker Classes	claim
54	105	Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems	claim
55	105	Linear State-Space Models for Blind Source Separation	claim
56	105	Some Theory for Generalized Boosting Algorithms	claim
57	105	Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems	claim
58	104	Learning Image Components for Object Recognition	claim
59	103	MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals	claim
60	103	On Model Selection Consistency of Lasso	claim
61	103	Pattern Recognition for Conditionally Independent Data	claim
62	103	A Graphical Representation of Equivalence Classes of AMP Chain Graphs	claim
63	100	Learning Recursive Control Programs from Problem Solving	claim
64	99	Worst-Case Analysis of Selective Sampling for Linear Classification	claim
65	97	Learning Coordinate Covariances via Gradients	claim
66	97	Distance Patterns in Structural Similarity	claim
67	96	Rearrangement Clustering: Pitfalls, Remedies, and Applications	claim
68	96	A Hierarchy of Support Vector Machines for Pattern Detection	claim
69	93	An Efficient Implementation of an Active Set Method for SVMs	claim
70	92	Linear Programs for Hypotheses Selection in Probabilistic Inference Models	claim
71	91	QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines	claim
72	90	Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition	claim
73	89	Lower Bounds and Aggregation in Density Estimation	claim
74	89	Maximum-Gain Working Set Selection for SVMs	claim
75	87	Second Order Cone Programming Approaches for Handling Missing and Uncertain Data	claim
76	85	Streamwise Feature Selection	claim
77	85	Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation	claim
78	82	Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting	claim
79	80	Using Machine Learning to Guide Architecture Simulation	claim
80	80	Large Scale Transductive SVMs	claim
81	79	Universal Kernels	claim
82	79	Estimation of Gradients and Coordinate Covariation in Classification	claim
83	78	Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach	claim
84	78	Learning Minimum Volume Sets	claim
85	62	Nonparametric Quantile Estimation	claim
86	61	In Search of Non-Gaussian Components of a High-Dimensional Distribution	claim

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