Pseudo-inverse linear discriminants for the improvement of overall classification accuracies
详细信息 查看全文
- 作者：Gao Daqi ; gaodaqi@ecust.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Dastagir Ahmed ; Guo Lili ; Wang Zejian ; Wang Zhe
- 关键词：Pseudo-inverse linear discriminants (PILDs) ; Fisher linear discriminants (FLDs) ; Threshold optimization ; Iterative learning ; Overall classification accuracies
- 刊名：Neural Networks
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：81
- 期：Complete
- 页码：59-71
- 全文大小：891 K
文摘
- •
Relationship between a PILD and an FLD is clarified based on overall accuracy.
- •
A PILD is not certainly equivalent to an FLD if the desired outputs are fixed.
- •
A PILD has nothing in common with an FLD when the desired outputs are changeable.
- •
Accuracies of PILDs are improved by optimal thresholds related to sizes and regions.
- •
The iterative learning strategy of PILDs is proposed, realized and verified.