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基于生成模型的 Q-learning二分类算法.
- Source :
-
Application Research of Computers / Jisuanji Yingyong Yanjiu . Nov2020, Vol. 37 Issue 11, p3326-3333. 5p. - Publication Year :
- 2020
-
Abstract
- For binary classification problems, the classifier based on the discriminant model usually searches for an optimal decision boundary, which is susceptible to data fluctuations. This paper proposed a Q-learning algorithm based on the generative model for binary classification ( BGQ-learning), which coded state and action separately and obtained corresponding decision functions, increasing the flexibility of decision space. And then it combined least squares temporal-difference (TD) algorithm and semi-gradient descent for parameter optimization, accelerating parameter convergence speed. This paper designed experiments to compare the performance of the proposed algorithm with three classical classifiers and a novel classifier. The test results on 7 data sets of the UCI database show that the proposed algorithm has excellent stability and classification accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Chinese
- ISSN :
- 10013695
- Volume :
- 37
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Application Research of Computers / Jisuanji Yingyong Yanjiu
- Publication Type :
- Academic Journal
- Accession number :
- 146716226
- Full Text :
- https://doi.org/10.19734/j.issn.1001-3695.2019.08.0277