DocumentCode :
2546466
Title :
Speeding up Discriminative Learning Quadratic Discriminant Function with sampling
Author :
Chen, Jingnian ; Xu, Li ; Zhao, Xiang ; Hu, Shunxiang
Author_Institution :
Dept. of Inf. & Comput. Sci., Shandong Univ. of Finance & Econ., Jinan, China
fYear :
2012
fDate :
29-31 May 2012
Firstpage :
896
Lastpage :
899
Abstract :
The Discriminative Learning Quadratic Discriminant Function (DLQDF) is a favorable method for multi-classification problems, especially for handwritten character recognition, due to its prominent classification effects. However, its training complexity is very high. To improve the efficiency of DLQDF while keeping its supper performance, this paper presents a sampling method-MBS (MQDF-Based Sampling) to speed up the process of parameter learning. Experiments show that MBS can effectively speed up the training of DLQDF while keeping classification accuracy.
Keywords :
handwritten character recognition; learning (artificial intelligence); pattern classification; quadratic programming; DLQDF; MQDF based sampling; handwritten character recognition; multiclassification problems; parameter learning; prominent classification effects; speeding up discriminative learning quadratic discriminant function; Accuracy; Character recognition; Complexity theory; Eigenvalues and eigenfunctions; Feature extraction; Training; Vectors; DLQDF; MQDF; multi-classification; sampling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
Type :
conf
DOI :
10.1109/FSKD.2012.6234013
Filename :
6234013
Link To Document :
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