Learning discrete probability distributions with a multi-resolution binary tree

被引：0

作者：

Sanchis, F. A. ^{[1
]}

Aznar, F. ^{[1
]}

Sempere, M. ^{[1
]}

Pujol, M. ^{[1
]}

Rizo, R. ^{[1
]}

机构：

[1] Univ Alicante, Dept Comp Sci & Artificial Intelligence, E-03080 Alicante, Spain

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS | 2006年 / 4224卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a method for learning and representing joint probabilistic distributions, using binary trees, is shown. This method could be used with the Bayesian Programming formalism, being a very useful tool when working with real world data. It has the advantage of learning unknown probabilistic distributions directly from raw data, and to remain more balanced than other previous methods. Finally, an application to learn a fuzzy control system, using this approach, will be presented.

引用

页码：472 / 479

页数：8

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