Title :
Reduced Training for Hierarchical Incremental Class Learning
Author :
Bao, Chunyu ; Guan, Sheng-Uei
Author_Institution :
Dept. of Electr. & Comput. Eng., National Univ. of Singapore
Abstract :
Hierarchical incremental class learning (HICL), proposed by Guan and Li in 2002, is a recently proposed task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but closer examination reveals areas for potential improvement. This paper presents an approach to improve the classification accuracy of HICL by applying the concept of reduced pattern training (RPT). The procedure for RPT is described and compared with the original training procedure. RPT systematically reduces the size of the training data set based on the order of sub-networks built. The results from benchmark classification problems show much promise for the improved model
Keywords :
learning (artificial intelligence); pattern classification; classifier system; hierarchical incremental class learning; pattern classification; reduced pattern training; reduced training set; task decomposition; Biological neural networks; Computer networks; Crosstalk; Decision making; Interference; Jacobian matrices; Neural networks; Parallel processing; Pattern classification; Training data; HICL; classifier systems; hierarchical learning; reduced training set;
Conference_Titel :
Cybernetics and Intelligent Systems, 2006 IEEE Conference on
Conference_Location :
Bangkok
Print_ISBN :
1-4244-0023-6
DOI :
10.1109/ICCIS.2006.252321
