Generalizing the Bias Term of Support Vector Machines
Refereed conference paper presented and published in conference proceedings


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AbstractBased on the study of a generalized form of representer theorem and a specific trick in constructing kernels, a generic learning model is proposed and applied to support vector machines. An algorithm is obtained which naturally generalizes the bias term of SVM. Unlike the solution of standard SVM which consists of a linear expansion of kernel functions and a bias term, the generalized algorithm maps predefined features onto a Hilbert space as well and takes them into special consideration by leaving part of the space unregularized when seeking a solution in the space. Empirical evaluations have confirmed the effectiveness from the generalization in classification tasks.
All Author(s) ListLi WY, Leung KS, Lee KH
Name of Conference20th International Joint Conference on Artificial Intelligence
Start Date of Conference06/01/2007
End Date of Conference12/01/2007
Place of ConferenceHyderabad
Country/Region of ConferenceIndia
Year2007
Month1
Day1
PublisherIJCAI-INT JOINT CONF ARTIF INTELL
Pages919 - 924
eISBN*****************
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

Last updated on 2020-29-05 at 02:20