Loading...
Please wait, while we are loading the content...
Similar Documents
Learning Heterogeneous Hidden Markov Random Fields
| Content Provider | CiteSeerX |
|---|---|
| Author | Liu, Jie Zhang, Chunming Burnside, Elizabeth |
| Abstract | Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowl-edge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorpo-rate the background knowledge. Simulations show that our algorithm is effective for learn-ing heterogeneous HMRFs and outperforms alternative binning methods. We learn a het-erogeneous HMRF in a real-world study. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Contrastive Divergence Learner Heterogeneous Hidden Markov Random Field Heterogeneous Hmrfs Het-erogeneous Hmrf Real-world Study Learn-ing Heterogeneous Hmrfs Biological Application Hidden Markov Random Field |
| Content Type | Text |
