DocumentCode
3306470
Title
Heuristic Attempts to Improve the Generalization Capacities in Learning SVMs
Author
State, Luminita ; Cocianu, Catalina ; Mircea, Marinela
Author_Institution
Dept. of Comput. Sci., Univ. of Pitesti, Pitesti, Romania
fYear
2012
fDate
8-10 Aug. 2012
Firstpage
108
Lastpage
113
Abstract
The paper reports some new variants of gradient ascent type in learning SVMs. The theoretical development is presented in the third section of the paper. The performance analysis of the proposed variants, in terms of recognition accuracy and generalization capacity, is experimentally evaluated and the results are presented and commented in the final part of the paper.
Keywords
gradient methods; learning (artificial intelligence); support vector machines; analysis; generalization capacity; gradient ascent type; heuristic attempts; learning SVM; recognition accuracy; Accuracy; Feature extraction; Indexes; Kernel; Linear programming; Reliability; Support vector machines; classification; gradient method; kernel functions; recognition; supervised learning; support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), 2012 13th ACIS International Conference on
Conference_Location
Kyoto
Print_ISBN
978-1-4673-2120-4
Type
conf
DOI
10.1109/SNPD.2012.37
Filename
6299266
Link To Document