摘要： 为达到更好的分类效果,提出一种基于邻域相似则序列相似猜想的序列分类方法,将样本序列所定义的有约束隐马尔可夫模型(HMM)空间转换到无约束HMM空间,在标准HMM处提取邻域信息,并将所有邻域信息导入到SVM中进行分类。实验结果表明,与其他经典序列分类方法相比 ,该方法能较大程度地提高分类效果及速度,同时也验证了最初猜想的正确性。

关键词: 序列分类, 无约束空间, 邻域信息, 隐马尔可夫模型, 支持向量机

Abstract: A sequence classification methodology is proposed based on a conjecture that neighborhood’s similarity results in sequence’s similarity.The constrained Hidden Markov Model(HMM) space defined by sample is transformed to unconstrained HMM space.The neighborhood information is extracted at the standard HMM,and is imported to the SVM.Experimental results show that compared with other classical sequence classification methods,the proposed methodology can indeed greatly improve accuracy or speed.Meanwhile,the results also validate the original conjecture.

Key words: sequence classification, unconstrained space, neighborhood information, Hidden Markov Model(HMM), Support Vector Machine(SVM)

中图分类号: 

• TP311