Using greedy algorithm to learn graphical model for digit recognition
Graphical model, the marriage between graph theory and probability theory, has been drawing increasing attention because of its many attractive features. In this paper, we consider the problem of learning the structure of graphical model based on observed data through a greedy forward-backward algor...
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ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-281312015-09-20T17:28:58ZUsing greedy algorithm to learn graphical model for digit recognitionYang, JisongGraphical modelMarkov random fieldGraphical model, the marriage between graph theory and probability theory, has been drawing increasing attention because of its many attractive features. In this paper, we consider the problem of learning the structure of graphical model based on observed data through a greedy forward-backward algorithm and with the use of learned model to classify the data into different categories. We establish the graphical model associated with a binary Ising Markov random field. And model selection is implemented by adding and deleting edges between nodes. Our experiments show that: compared with previous methods, the proposed algorithm has better performance in terms of correctness rate and model selection.text2015-01-20T19:53:39Z2014-122014-12-17December 20142015-01-20T19:53:39ZThesisapplication/pdfhttp://hdl.handle.net/2152/28131en |
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NDLTD |
| language |
en |
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Others
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NDLTD |
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Graphical model Markov random field |
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Graphical model Markov random field Yang, Jisong Using greedy algorithm to learn graphical model for digit recognition |
| description |
Graphical model, the marriage between graph theory and probability theory, has been drawing increasing attention because of its many attractive features. In this paper, we consider the problem of learning the structure of graphical model based on observed data through a greedy forward-backward algorithm and with the use of learned model to classify the data into different categories. We establish the graphical model associated with a binary Ising Markov random field. And model selection is implemented by adding and deleting edges between nodes. Our experiments show that: compared with previous methods, the proposed algorithm has better performance in terms of correctness rate and model selection. === text |
| author |
Yang, Jisong |
| author_facet |
Yang, Jisong |
| author_sort |
Yang, Jisong |
| title |
Using greedy algorithm to learn graphical model for digit recognition |
| title_short |
Using greedy algorithm to learn graphical model for digit recognition |
| title_full |
Using greedy algorithm to learn graphical model for digit recognition |
| title_fullStr |
Using greedy algorithm to learn graphical model for digit recognition |
| title_full_unstemmed |
Using greedy algorithm to learn graphical model for digit recognition |
| title_sort |
using greedy algorithm to learn graphical model for digit recognition |
| publishDate |
2015 |
| url |
http://hdl.handle.net/2152/28131 |
| work_keys_str_mv |
AT yangjisong usinggreedyalgorithmtolearngraphicalmodelfordigitrecognition |
| _version_ |
1716824174286077952 |