BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
HMM Based On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical methods for speech recognition
Statistical methods for speech recognition
Hidden Markov Model Based Continuous Online Gesture Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Fast inference and learning in large-state-space HMMs
ICML '05 Proceedings of the 22nd international conference on Machine learning
CarpeDiem: an algorithm for the fast evaluation of SSL classifiers
Proceedings of the 24th international conference on Machine learning
Joint scene classification and segmentation based on hidden Markov model
IEEE Transactions on Multimedia
Fast likelihood search for hidden Markov models
ACM Transactions on Knowledge Discovery from Data (TKDD)
| Hi-index | 0.00 |
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.