文献类型：期刊文献

英文题名：Function approximation model ensembles and their application to the simultaneous determination of sample categories and positions

作者：Gao, Daqi[1]; Sun, Xiaoning[1]

机构：[1] Department of Computer Science, East China University of Science and Technology, Shanghai 200237, China

年份：2007

起止页码：1918

外文期刊名：IEEE International Conference on Neural Networks - Conference Proceedings

收录：EI(收录号：20083811574585)

语种：英文

外文关键词：Neural networks

摘要：This paper uses multiple approximation model ensembles to solve a multi-input multi-output learning task. An ensemble is on behalf of a specified class, and composed of several multi-input single-output (MISO) approximation models. An MISO model may be either a multivariable cubic polynomial, or a multi-variable quartic polynomial, or a single-hidden-layer perceptron. The number of ensembles is equal to that of the existing classes, and all the members in an ensemble are trained only by the samples from the represented category. The ensemble in which all the members have the most identical viewpoint finally determines the label and position of one sample. The "most identical viewpoint" can be scaled by the corrected relative standard deviation. The proposed method is verified to be effective by a synthetic dataset. ?2007 IEEE.