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An Extended Approach to Learning Recursive Probability Trees from Data
Author(s) -
Cano Andrés,
GómezOlmedo Manuel,
PérezAriza Cora B.
Publication year - 2015
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21703
Subject(s) - probabilistic logic , computer science , representation (politics) , machine learning , artificial intelligence , probabilistic database , graphical model , probability distribution , theoretical computer science , data mining , mathematics , database theory , statistics , politics , relational database , political science , law
Rlatecursive probability trees (RPTs) offer a flexible framework for representing the probabilistic information in probabilistic graphical models. This structure is able to provide a compact representation of the distribution it encodes by specifying most of the types of independencies that can be found in a probability distribution. The real benefit of this representation heavily depends on the ability of learning such independencies from data. In this paper, we expand our approach at learning RPTs from data by extending an existing greedy methodology for retrieving small RPTs from probabilistic potentials. We test the performance of the algorithm by learning from different databases, both real and handcrafted, and we compare the performance for different databases sizes.