Fuzzy measures in Hidden Markov Models

被引：0
作者：
Mukherjee, S
Mitra, S [1 ]
机构：
[1] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India
[2] Assoc Studies Computat Biol, Kolkata 700018, India
来源：
NEURAL NETWORK WORLD | 2005年 / 15卷 / 03期
关键词：
fuzzy measure; Hidden Markov Models; computational biology;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recently Hidden Markov Models (HMMs) and Stochastic grammar models have been extensively employed in various fields like computational biology, speech recognition, gesture recognition and text processing with the problems being modelled using classical probability measures. Fuzzy measure is an extension to the classical measure theory with promising applications in various areas. Some generalized HMMs have been developed with fuzzy measures for the speech recognition problem. The well known algorithms used for HMMs are thereby shown to execute faster. In this article, we discuss the classical and fuzzy measure formulation of HMMs, followed by applications in speech recognition using fuzzy measures. We also indicate the possible scope for their application to Bioinformatics.
引用
收藏
页码：269 / 280
页数：12
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