A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Incremental construction of structured hidden Markov models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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This paper presents an extensive evaluation, on artificial datasets, of EDY, an unsupervised algorithm for automatically synthesizing a Structured Hidden Markov Model (S-HMM) from a database of sequences. The goal of EDY is capturing the stochastic process by which the observed data was generated. The SHMM is a sub-class of Hidden Markov Model that exhibits a quasi-linear computational complexity and is well suited to real-time problems of process/user profiling. The datasets used for the evaluation are available on the web http://www.edygroup.di.unipmn.it. They are a proposal benchmark for the deep-testing and comparing of tools developed for analysis of temporal (spatial) sequences in which the objective is to reconstruct the generative model from which the sequences originated.