Markov trees generalize naturally to bounded tree-width Markov networks, onwhich exact computations can still be done efficiently. However, learning themaximum likelihood Markov network with tree-width greater than 1 is NP-hard, sowe discuss a few algorithms for approximating the optimal Markov network. Wepresent a set of methods for training a density estimator. Each method isspecified by three arguments: tree-width, model scoring metric (maximumlikelihood or minimum description length), and model representation (using onejoint distribution or several class-conditional distributions). On thesemethods, we give empirical results on density estimation and classificationtasks and explore the implications of these arguments.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30511 |
| Date | 30 December 2004 |
| Creators | Liang, Percy, Srebro, Nathan |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 10 p., 10714507 bytes, 473643 bytes, application/postscript, application/pdf |
| Relation | Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory |
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