文献类型：期刊文献

英文题名：Pseudo-inverse linear discriminants for the improvement of overall classification accuracies

作者：Gao Daqi[1];Ahmed, Dastagir[1];Guo Lili[1];Wang Zejian[1];Wang Zhe[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：81

起止页码：59

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20162702550516);WOS:【SCI-EXPANDED(收录号:WOS:000381833300007)】；

基金：This work is funded by the National Science Foundation of China (NSFC) under Grant Nos. 21176077, 61272198 and 60675027, the High-Tech Development Program of China (863) under Grant No. 2006AA10Z315, and the Open Funding Project of the State Key Laboratory of Bioreactor Engineering under Grant No. 2011006.

语种：英文

外文关键词：Pseudo-inverse linear discriminants (PILDs); Fisher linear discriminants (FLDs); Threshold optimization; Iterative learning; Overall classification

摘要：This paper studies the learning and generalization performances of pseudo-inverse linear discriminant (PILDs) based on the processing minimum sum-of-squared error ((MSE)-E-2) and the targeting overall classification accuracy (OCA) criterion functions. There is little practicable significance to prove the equivalency between a PILD with the desired outputs in reverse proportion to the number of class samples and an FLD with the totally projected mean thresholds. When the desired outputs of each class are assigned a fixed value, a PILD is partly equal to an FLD. With the customarily desired outputs (1, -1), a practicable threshold is acquired, which is only related to sample sizes. If the desired outputs of each sample are changeable, a PILD has nothing in common with an FLD. The optimal threshold may thus be singled out from multiple empirical ones related to sizes and distributed regions. Depending upon the processing MS2E criteria and the actually algebraic distances, an iterative learning strategy of PILD is proposed, the outstanding advantages of which are with limited epoch, without learning rate and divergent risk. Enormous experimental results for the benchmark datasets have verified that the iterative PILDs with optimal thresholds have good learning and generalization performances, and even reach the top OCAs for some datasets among the existing classifiers. (C) 2016 Elsevier Ltd. All rights reserved.