Synthesis and Recognition of Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data-driven design of HMM topology for online handwriting recognition
Hidden Markov models
Hybrid Pattern Recognition Using Markov Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
BWT-based efficient shape matching
Proceedings of the 2007 ACM symposium on Applied computing
An algorithm for the dynamic inference of hidden Markov models (DIHMM)
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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Inference of Markov networks from finite sets of sample strings is formulated using dynamic programming. Strings are installed in a network sequentially via optimal string-to-network alignments computed with a dynamic programming matrix, the cost function of which uses relative frequency estimates of transition probabilities to emphasize landmark substrings common to the sample set. Properties of an inferred network are described and the method is illustrated with artificial data and with data representing banded human chromosomes.