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单调性约束的规则学习

Rule Learning with Monotonicity Constraints
课程网址: http://videolectures.net/icml09_kotlowski_rlwmc/  
主讲教师: Wojciech Kotlowski
开课单位: 波兹南理工大学
开课时间: 2009-08-26
课程语种: 英语
中文简介:
在具有单调性约束的序数分类中,假设类标签应随着属性值的增加而增加。在本文中,我们的目标是从统计学的角度将单调性约束的学习方法形式化,从而产生学习规则集合的算法。该算法首先使用非参数分类过程对数据进行“单调化”,然后生成与训练集一致的规则集合。该过程通过理论分析得到证实,并在计算实验中得到验证。
课程简介: In the ordinal classification with monotonicity constraints, it is assumed that the class label should increase with increasing values on the attributes. In this paper we aim at formalizing the approach to learning with monotonicity constraints from statistical point of view, which results in the algorithm for learning rule ensembles. The algorithm first ”monotonizes” the data using a nonparametric classification procedure and then generates rule ensemble consistent with the training set. The procedure is justified by a theoretical analysis and verified in a computational experiment.
关 键 词: 序数分类; 学习规则集合; 非参数分类
课程来源: 视频讲座网
最后编审: 2019-04-23:lxf
阅读次数: 94