Feature Weighting for RBFNN Based on Genetic Algorithm and Localized Generalization Error Model

2017 ◽  
Author(s):  
Tianyu Chen ◽  
Ting Wang ◽  
Wing W. Y. Ng ◽  
Jianjun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Error Model ◽  
Feature Weighting ◽  
Generalization Error ◽  
Localized Generalization Error

Training an extreme learning machine by localized generalization error model

2018 ◽  
Vol 22 (11) ◽  
pp. 3477-3485 ◽  
Author(s):  
Hong Zhu ◽  
Eric C. C. Tsang ◽  
Jie Zhu
Keyword(s):  
Extreme Learning Machine ◽  
Error Model ◽  
Generalization Error ◽  
Learning Machine ◽  
Localized Generalization Error

THE LOCALIZED GENERALIZATION ERROR MODEL FOR SINGLE LAYER PERCEPTRON NEURAL NETWORK AND SIGMOID SUPPORT VECTOR MACHINE

2008 ◽  
Vol 22 (01) ◽  
pp. 121-135 ◽  
Author(s):  
WING W. Y. NG ◽  
DANIEL S. YEUNG ◽  
ERIC C. C. TSANG
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Error Bound ◽  
Single Layer ◽  
Error Model ◽  
Support Vector ◽  
Generalization Error ◽  
Sensitivity Measure ◽  
Stochastic Sensitivity ◽  
Localized Generalization Error

We had developed the localized generalization error model for supervised learning with minimization of Mean Square Error. In this work, we extend the error model to Single Layer Perceptron Neural Network (SLPNN) and Support Vector Machine (SVM) with sigmoid kernel function. For a trained SLPNN or SVM and a given training dataset, the proposed error model bounds above the error for unseen samples which are similar to the training samples. As the major component of the localized generalization error model, the stochastic sensitivity measure formula for perceptron neural network derived in this work has relaxed the assumptions of same distribution for all inputs and each sample perturbed only once in previous works. These make the sensitivity measure applicable to pattern classification problems. The stochastic sensitivity measure of SVM with Sigmoid kernel is also derived in this work as a component of the localized generalization error model. At the end of this paper, we discuss the advantages of the proposed error bound over existing error bound.

Dynamic fusion method using Localized Generalization Error Model

2012 ◽  
Vol 217 ◽  
pp. 1-20 ◽  
Author(s):  
Patrick P.K. Chan ◽  
Daniel S. Yeung ◽  
Wing W.Y. Ng ◽  
Chih Min Lin ◽  
James N.K. Liu
Keyword(s):  
Error Model ◽  
Fusion Method ◽  
Generalization Error ◽  
Localized Generalization Error
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