DocumentCode
3583119
Title
Critical vector learning to construct sparse kernel modeling with PRESS statistic
Author
Gao, Jun-Bin ; Zhang, Lei ; Shi, Dequan
Author_Institution
Sch. of Math. Stat. & Comput. Sci., New England Univ., Armidale, NSW, Australia
Volume
5
fYear
2004
Firstpage
3223
Abstract
A novel critical vector (CV) regression algorithm is proposed in the paper based on our previous work and PRESS statistics. The proposed regularized CV algorithm finds critical vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm.
Keywords
computational complexity; greedy algorithms; learning (artificial intelligence); regression analysis; vectors; critical vector learning; critical vector regression algorithm; greedy process; orthogonalization; predicted residual sums of squares statistics; sparse kernel modeling; time complexity; Australia; Computer science; Kernel; Machine learning; Mathematical model; Mathematics; Paper technology; Statistics; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1378591
Filename
1378591
Link To Document