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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1378591
