DocumentCode :
2927675
Title :
Based on the Reinforcement Learning Association Rules Recommendation Study
Author :
Wang, Jinqiao ; Yang, Qing ; Zhu, Li ; Sun, Junli
Author_Institution :
Dept. of Comput. Sci., Huazhong Normal Univ., Wuhan, China
fYear :
2009
fDate :
12-14 Oct. 2009
Firstpage :
392
Lastpage :
395
Abstract :
Reinforcement learning is an important method of machine learning. This paper using the graph theory to express varieties of knowledge points, which their´s relationship is expressed by the graph of topological graph. Applied the Technology of association rule Recommendation to deal with the relationship between these knowledge points, give the corresponding of the recommendation work flow chart. In the paper data tables used to store the knowledge points, the algorithm to demonstrate the technical of association rule Recommendation feasibility and rationality.
Keywords :
data mining; graph theory; learning (artificial intelligence); association rules; graph theory; machine learning; reinforcement learning; topological graph; Association rules; Computer science; Data mining; Flowcharts; Graph theory; Intelligent robots; Machine learning; Resonance light scattering; Signal generators; Testing; Recommendation Systems data mining; association rules; reinforcement learning; topological graph;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on
Conference_Location :
Zhuhai
Print_ISBN :
978-0-7695-3810-5
Type :
conf
DOI :
10.1109/SKG.2009.19
Filename :
5370016
Link To Document :
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