Applications of feature space distance measures in training support vector classifiers

Applications of feature space distance measures in training support vector classifiers

博士 === 國立臺灣大學 === 電機工程學研究所 === 96 === Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. The most popular method to decide the parameters is the grid search method. In the training process, classifiers are trained with differ...

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Bibliographic Details
Main Authors:	Kuo-Ping Wu, 吳國賓
Other Authors:	王勝德
Format:	Others
Language:	en_US
Published:	2008
Online Access:	http://ndltd.ncl.edu.tw/handle/66064360761867794430

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