Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | null | Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies, MIMT 2010 |
Keywords | Field | DocType |
association rule recommendation,knowledge point,topological graph,reinforcement learning,paper data table,learning (artificial intelligence),recommendation study,association rule recommendation feasibility,recommendation work flow chart,important method,recommendation systems data mining,data mining,graph theory,association rules,machine learning,reinforcement learning association rules,databases,data structures,correlation,recommender system,learning artificial intelligence,knowledge engineering,association rule | Graph theory,Data mining,Data structure,Rationality,Computer science,Association rule learning,Knowledge engineering,Chart,Artificial intelligence,Machine learning,Topological graph,Reinforcement learning | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-0-7695-3810-5 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinqiao Wang | 1 | 804 | 89.03 |
| Qing Yang | 2 | 0 | 0.68 |
| Li Zhu | 3 | 0 | 4.06 |
| JunLi Sun | 4 | 0 | 0.34 |