SPIRAL: efficient and exact model identification for hidden Markov models
SPIRAL: efficient and exact model identification for hidden Markov models
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SPIRAL：隐马尔可夫模型的高效、准确的模型识别
DOI：
10.1145/1401890.1401924
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
M. Yamamuro
中科院分区：
文献类型：
--
作者：
Y. Fujiwara;Yasushi Sakurai;M. Yamamuro
隐马尔可夫模型(HMM)在语音识别、神经学和生物信息学等领域得到了广泛的关注，因为已经出现了许多使用HMM的应用。这项工作的目标是有效和正确地识别给定数据集中的模型，该模型产生相对于查询序列具有最高似然的状态序列。提出了一种针对隐马尔可夫模型数据集的快速搜索方法--螺旋。为了减少搜索成本，螺旋算法通过在估计似然时应用逐次逼近来有效地剪除大量的搜索候选者。为了验证螺旋算法的有效性，我们进行了多个实验。实验结果表明，螺旋算法的速度是朴素算法的500多倍。
Hidden Markov models (HMMs) have received considerable attention in various communities (e.g, speech recognition, neurology and bioinformatic) since many applications that use HMM have emerged. The goal of this work is to identify efficiently and correctly the model in a given dataset that yields the state sequence with the highest likelihood with respect to the query sequence. We propose SPIRAL, a fast search method for HMM datasets. To reduce the search cost, SPIRAL efficiently prunes a significant number of search candidates by applying successive approximations when estimating likelihood. We perform several experiments to verify the effectiveness of SPIRAL. The results show that SPIRAL is more than 500 times faster than the naive method.