DocumentCode :
1749271
Title :
Large margin kernel pocket algorithm
Author :
Xu, Jianhua ; Zhang, Xuegong ; Li, Yanda
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Volume :
2
fYear :
2001
fDate :
2001
Firstpage :
1480
Abstract :
Two attractive advantages of SVM are the ideas of kernels and of large margin. As a linear learning machine, the original pocket algorithm can handle both linearly and nonlinearly separable problems. In order to improve its classification ability and control its generalization, we generalize the original pocket algorithm by using kernels and adding a margin criterion, and propose its kernel and large margin version, which can be referred to as large margin kernel pocket algorithm (LMKPA). The objective is to maximize both the number of correctly classified samples and the distance between the separating hyperplane and those correctly classified samples closest to the hyperplane, in the feature space realized with the kernels. This new algorithm only utilizes an iterative procedure to implement kernel idea and large margin simultaneously. For the linearly separable problems, LMKPA can find a solution that is not only without error, but also almost equivalent to that of SVM with the large-margin goal. For linearly nonseparable problems, its performance is also very close to that of SVM. Experiments in numeral computation aspects show that the performance of LMKPA is close to that of SVM but the algorithm is much simpler
Keywords :
generalisation (artificial intelligence); learning automata; optimisation; pattern classification; perceptrons; LMKPA; SVM; classification ability; generalization control; iterative procedure; large margin; large margin kernel pocket algorithm; large-margin goal; linear learning machine; linearly nonseparable problems; linearly separable problems; margin criterion; nonlinearly separable problems; separating hyperplane; support vector machine; Algorithm design and analysis; Intelligent systems; Iterative algorithms; Kernel; Learning systems; Machine learning; Machine learning algorithms; Robustness; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.939581
Filename :
939581
Link To Document :
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