Localized generalization error model and its application to architecture selection for radial basis function neural network.

Localized generalization error model and its application to architecture selection for radial basis function neural network. Research Abstract Details 

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Localized generalization error model and its application to architecture selection for radial basis function neural network. Abstract Text:

Daniel S Yeung,Wing W Y Ng,Defeng Wang,Eric C C Tsang,Xi-Zhao Wang,Daniel S Yeung,Wing W Y Ng,Defeng Wang,Eric C C Tsang,Xi-Zhao Wang,

The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving problems and treat unseen samples near the training samples to be more important. In this paper, we propose a localized generalization error model which bounds from above the generalization error within a neighborhood of the training samples using stochastic sensitivity measure. It is then used to develop an architecture selection technique for a classifier with maximal coverage of unseen samples by specifying a generalization error threshold. Experiments using 17 University of California at Irvine (UCI) data sets show that, in comparison with cross validation (CV), sequential learning, and two other ad hoc methods, our technique consistently yields the best testing classification accuracy with fewer hidden neurons and less training time.

Localized generalization error model and its application to architecture selection for radial basis function neural network. Publishing Authors By Initials

DS Yeung,WW Ng,D Wang,EC Tsang,XZ Wang,DS Yeung,WW Ng,D Wang,EC Tsang,XZ Wang,

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Localized generalization error model and its application to architecture selection for radial basis function neural network. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: IEEE transactions on neural networks / a publicati

VOLUME: 18

Page Numbers: 1294-305

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ISSN: 1045-9227

DAY: 28

MONTH: Sep

YEAR: 2007

Localized generalization error model and its application to architecture selection for radial basis function neural network. Information

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LANGUAGE: eng

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AFFILIATION: Media and Life Science, Department of Computer Science and Technology, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China. csdaniel@comp.polyu.edu.hk

Country: United States

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MEDLINETA: IEEE Trans Neural Netw

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