Name
Abstract | ||
|---|---|---|
Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on thi... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1162/neco_a_01033 | Neural Computation |
Field | DocType | Volume |
Divergence,Activity recognition,Pattern recognition,Third order,Artificial intelligence,Data sequences,Score,Hidden Markov model,Machine learning,Mathematics,Computational complexity theory | Journal | 30 |
Issue | ISSN | Citations |
1 | 0899-7667 | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rasmus Troelsgaard | 1 | 0 | 0.34 |
| Lars Kai Hansen | 2 | 2776 | 341.03 |