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Inducing Hidden Markov Models to Model Long-Term Dependencies (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Callut, Jerome Callut, Jérôme Dupont, Pierre |
| Description | In 16th European Conference on Machine Learning (ECML), number 3720 in Lecture Notes in Artificial Intelligence We propose in this paper a novel approach to the induction of the structure of Hidden Markov Models. The induced model is seen as a lumped process of a Markov chain. It is constructed to fit the dynamics of the target machine, that is to best approximate the stationary distribution and the mean first passage times observed in the sample. The induction relies on non-linear optimization and iterative state splitting from an initial order one Markov chain. |
| File Format | |
| Language | English |
| Publisher | Springer Verlag |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Passage Time Induction Relies Novel Approach Initial Order Iterative State Splitting Stationary Distribution Target Machine Lumped Process Induced Model Markov Chain Model Long-term Dependency Non-linear Optimization Hidden Markov Model |
| Content Type | Text |
| Resource Type | Article |
