DocumentCode :
2414446
Title :
Cascade Jump Support Vector Machine Classifiers
Author :
Ravindran, Sourabh ; Anderson, David V. ; Rehg, James
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA
fYear :
2005
fDate :
28-28 Sept. 2005
Firstpage :
135
Lastpage :
139
Abstract :
In this paper we present a new support vector machine (SVM) based classifier that is able to achieve better generalization as compared to the standard SVM. Better generalization is achieved by using a cascade of modified proximal SVMs to remove simpler examples before presenting the difficult examples to a more complex SVM. The cascade structure uses the discrimination afforded by different feature spaces (by using different kernels) to simplify the classification task
Keywords :
feature extraction; generalisation (artificial intelligence); pattern classification; support vector machines; cascade jump support vector machine classifier; cascade structure; feature space; generalization; modified proximal SVM; pattern classification; Educational institutions; Kernel; Polynomials; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2005 IEEE Workshop on
Conference_Location :
Mystic, CT
Print_ISBN :
0-7803-9517-4
Type :
conf
DOI :
10.1109/MLSP.2005.1532888
Filename :
1532888
Link To Document :
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