中国电子科技(英文版)2004，Vol.2Issue(1)：63-68,6.
Partial Oblique Projection Learning for Optimal Generalization
Partial Oblique Projection Learning for Optimal Generalization
LIU Benyong 1ZHANG Jing2
作者信息
- 1. School of Electronic Engineering,UESTC Chengdu 610054 China
- 2. The Information Center of Sichuan Radio and Television University Chengdu 610073 China
- 折叠
摘要
Abstract
In practice,it is necessary to implement an incremental and active learning for a learning method.In terms of such implementation,this paper shows that the previously discussed S-L projection learning is inappropriate to constructing a family of projection learning,and proposes a new version called partial oblique projection (POP) learning.In POP learning,a function space is decomposed into two complementary subspaces,so that functions belonging to one of the subspaces can be completely estimated in noiseless case;while in noisy case,the dispersions are set to be the smallest.In addition,a general form of POP learning is presented and the results of a simulation are given.关键词
supervised learning/generalization/S-L projection learning/partial oblique projection learningKey words
supervised learning/generalization/S-L projection learning/partial oblique projection learning分类
数理科学引用本文复制引用
LIU Benyong,ZHANG Jing..Partial Oblique Projection Learning for Optimal Generalization[J].中国电子科技(英文版),2004,2(1):63-68,6.
