Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33680
Title: Pilot study on the localized generalization error model for single layer perceptron neural network
Authors: Ng, WWY
Yeung, DS
Tsang, ECC
Keywords: Localized Generalization Error Bound
Single Layer Perceptron Neural Network
Issue Date: 2006
Publisher: IEEE
Source: 2006 International Conference on Machine Learning and Cybernetics, 13-16 August 2006, Dalian, China, p. 3078-3082 How to cite?
Abstract: 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). For a trained SLPNN and a given training dataset, the proposed error model bounds above the error for unseen samples which are similar to the training samples. This pilot study is the important first step of investigating localized generalization error models for multilayer perceptron neural networks and support vector machines with sigmoid kernel functions. The characteristics of the error model for SLPNN and how to compare SLPNNs' generalization capabilities using the error model are also discussed in this paper
URI: http://hdl.handle.net/10397/33680
ISBN: 1-4244-0061-9
DOI: 10.1109/ICMLC.2006.258370
Appears in Collections:Conference Paper

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