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
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;
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
DOI :
10.1109/SNPD.2012.37
