A Special Supervised Learning Algorithm and Its Applications
The supervised learning algorithm with one normal sample in every class is used in many domains. However the algorithm isn’t normal and will influence the result. Because there is only one normal sample in every class, the learning stage is omitted. Very strong information conditions make this recognition algorithm different from other supervised learning algorithms. In this paper we point out: The usual method using ‘certain distance’ as similarity measure between samples and normal class samples is not suitable for present information conditions. The best choice is to use membership as similarity measure between index value and classes. However, topological space structure, corresponding algebraic properties and normalization method of the membership must be normalized. The conversion of index membership to sample membership is a pure mathematical problem. The index weight is determined by the information entropy of index value membership and has nothing to do with the decider. A new supervised learning algorithm is proposed and water quality evaluation is solved by this method. The evaluation results using different evaluation methods show the new method is effective.
supervised learning normal sample similarity measure index classification weight membership conversion insert
Yanjun Pang Jiqiang Chen Nianpeng Wang
College of Science Hebei University of Engineering Handan, China
国际会议
2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)
沈阳
英文
1-5
2009-08-12（万方平台首次上网日期，不代表论文的发表时间）