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This book introduce a web page layout weighting scheme for HTML pre-process to deal with degree of importance of information from user s visual perception point of view. By mapping from position, size, and depth of a web page to a degree of freedom, the layout weighting scheme generates a continuous right skew distribution to estimate the importance of a block on a web page of a website, so that we can weight importance of information components in a block for making classification more accuracy. Along with this layout weighting scheme, multi-class support vector machine classifiers are…mehr

Produktbeschreibung
This book introduce a web page layout weighting scheme for HTML pre-process to deal with degree of importance of information from user s visual perception point of view. By mapping from position, size, and depth of a web page to a degree of freedom, the layout weighting scheme generates a continuous right skew distribution to estimate the importance of a block on a web page of a website, so that we can weight importance of information components in a block for making classification more accuracy. Along with this layout weighting scheme, multi-class support vector machine classifiers are applied to implement classification architecture for websites classification. This book considers that selection of multi-class SVM and kernel functions should be different due to the different application domains, therefore this book proposed a layout weighting scheme and with different kernel functions used by multi-class support vector machine classifiers in experiments in order to find the best performance approach.
Autorenporträt
Kai-Liang Ko received the bachelor of Information Management from Shih Hsin University and the masters of Information Management from National Chung Cheng University, Taipei, Taiwan, R.O.C., in 2004 and 2006. His research interests include machine learning, data mining, and web2.0 application.