关键词: 模式识别, 二分类, 多观测样本, LS-SVM算法

Abstract: To solve the problem of binary classification based on multi-observation sets, a novel LS-SVM based classification algorithm for multi-observation sets is proposed. In each classification, the object is represented by a multi-observation set, and then it makes an assumption about the class of the multi-observation set. Adding the assumption condition to the constraints of optimization problems in LS-SVM, the class is determined by comparing the different classification errors, which are obtained on different assumptions about the class of the multi-observation set. The method does not require training a classifier before classifications, considerating the labeled samples and multiple observation samples simultaneously and taking advantage of continuity law of similar samples in the feature space. Experiments show that the proposed method is valid and efficient.

Key words: pattern recognition, binary classification, multiple observation samples, LS-SVM