Particle Swarm Optimization in Exploratory Data Analysis

被引：0

作者：

Wu, Ying ^{[1
]}

Fyfe, Colin ^{[2
]}

机构：

[1] Natl Univ Ireland Univ Coll Cork, Coastal & Marine Resources Ctr, Cork, Ireland

[2] Univ West Scotland, Appl Computat Intelligence Res Unit, Paisley, Renfrew, Scotland

来源：

TRENDS IN APPLIED INTELLIGENT SYSTEMS, PT II, PROCEEDINGS | 2010年 / 6097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：

We discuss extensions of particle swami based optimization (PSO) algorithms in the context of exploratory data analysis In particular, we apply these extensions to principal component, analysis. exploratory projection pursuit and topology preserving mappings Our extensions include combining PSO algorithms with stochastic sampling and a form of remforcement learning known as Q-learing We illustrate on a vamety of artificial data sets and show that our new results ate batter than previous results on such data sets

引用

页码：257 / +

页数：3

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