DocumentCode
394192
Title
SVM maximizing margin in the input space
Author
Akaho, Shotaro
Author_Institution
Neurosci. Res. Inst., Nat. Inst. of Adv. Ind. & Sci. Technol., Tsukuba, Japan
Volume
2
fYear
2002
fDate
18-22 Nov. 2002
Firstpage
1069
Abstract
We propose a new type of support vector machine (SVM) that maximizes the margin in the input space, not in the feature space. Parameters are initialized by the original SVM, and they are updated by solving a quadratic programming problem iteratively. The derived algorithm preserves the sparsity of support vectors. It is also shown that the original SVM can be seen as a special case. The algorithm is confirmed to work by a simple simulation.
Keywords
iterative methods; quadratic programming; support vector machines; input space; iterative solution; margin maximization; parameter initialization; quadratic programming problem; support vector machines; Aerospace industry; Constraint optimization; Foot; Iterative algorithms; Neuroscience; Pattern recognition; Quadratic programming; Space technology; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN
981-04-7524-1
Type
conf
DOI
10.1109/ICONIP.2002.1198224
Filename
1198224
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