The incremental learning algorithm with support vector machine based on hyperplane-distance

Cunhe Li

doi:10.1007/s10489-009-0176-9

2009-04-01

PRE

李村合 (Cunhe Li)

Kangwei Liu *

Hongxia Wang

DOI：10.1007/s10489-009-0176-9

原文链接

摘要

En 中文

This paper proves the problem of losing incremental samples' information of the present SVM incremental learning algorithm from both theoretic and experimental aspects, and proposes a new incremental learning algorithm with support vector machine based on hyperplane-distance. According to the geometric character of support vector, the algorithm uses Hyperplane-Distance to extract the samples, selects samples which are most likely to become support vector to form the vector set of edge, and conducts the support vector machine training on the vector set. This method reduces the number of training samples and effectively improves training speed of incremental learning. The results of experiment performed on Chinese webpage classification show that this algorithm can reduce the number of training samples effectively and accumulate historical information. The HD-SVM algorithm has higher training speed and better precision of classification.

Keyword:

Support vector machine

Incremental learning

Hyperplane-distance

Chinese webpage classification