Title :
Some Improvements of t-Cherry Junction Trees
Author :
Kovács, Edith ; Szántai, Tamás
Author_Institution :
AVF Coll. of Manage., Budapest, Hungary
Abstract :
One of the important areas of machine learning is the development and use of probabilistic models for classification and prediction. In our earlier work we introduced a special kind of junction tree, based on a hypergraph structure called t-cherry tree and on some information theoretical concepts. In this paper we present a possibility for the improvement of these junction trees, by ldquocutting and refittingrdquo of the junction treepsilas branches. Both theoretical and experimental results demonstrate the improvement of the junction tree obtained after ldquobranch cutting and refittingrdquo.
Keywords :
learning (artificial intelligence); pattern classification; probability; trees (mathematics); branch cutting-and-refitting method; hypergraph structure; information theoretical concept; machine learning; pattern classification; probabilistic model; t-cherry junction tree; Artificial intelligence; Bayesian methods; Biomedical equipment; Learning systems; Machine learning; Markov random fields; Medical services; Probability distribution; Random variables; Tree graphs; Markov network; feature selection; junction tree; machine learning; t-cherry junction tree;
Conference_Titel :
Complexity and Intelligence of the Artificial and Natural Complex Systems, Medical Applications of the Complex Systems, Biomedical Computing, 2008. CANS '08. First International Conference on
Conference_Location :
Targu Mures, Mures
Print_ISBN :
978-0-7695-3621-7
DOI :
10.1109/CANS.2008.22