关键词: SVM, 增量学习, KKT条件, 壳向量

Abstract:

The traditional support vector machine (SVM) incremental algorithm in the learning process may give up the non-support vectors with implicit information,and requires the training of all the incremental samples.This paper presents a new incremental SVM learning algorithm based on KKT conditions and hull vectors.The algorithm makes use of the characteristics of hull vectors to retrain non-support vectors with implicit information,and it only add the samples violating the KKT conditions to the new training set.The experimental results from Abalone dataset and Balance Scale dataset show this algorithm has better classification effect in the datasets with more columns of properties.

Key words: SVM;incremental learning;KKT conditions;hull vectors