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An explicit algorithm for training support vector machines

delete1999-09-01
delete20
PRE
AI
D
Davide Mattera *
F
F. Palmieri
S
Simon Haykin
DOI：10.1109/97.782071delete
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Abstract

Abstract

En 中文
The support vector machine (SVM) constitutes one of the most powerful methods for constructing a mathematical model on the basis of a given number of training examples. SVM training requires that we solve a quadratic optimization problem; this step is usually performed by means of existing software packages, Such a black-box approach may be undesirable. In this paper we introduce a simple iterative algorithm for SVM training which compares well with some typical software packages, can be simply implemented, and has minimal memory requirements, It addresses the problem of regression estimation and utilizes ideas similar to those proposed in [1] for trraining binary SVM.
Keywords:
neural networks
nonlinear estimation
nonlinear systems
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Signal Processing Magazine cover
IEEE Signal Processing Magazine
IF:
9.6
Papers:
1.1W
Citations:
1.7W

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No organization information available