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Proceedings Paper

Support vector machine optimization via margin distribution analysis
Author(s): Donald Waagen; Mary Cassabaum; Harry A. Schmitt; Bruce Pollock
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Paper Abstract

Support Vector Machines (SVMs) have generated excitement and interest in the pattern recognition community due to their generalization, performance, and ability to operate in high dimensional feature spaces. Although SVMs are generated without the use of user-specified models, required hyperparameters, such as Gaussian kernel width, are usually user-specified and/or experimentally derived. This effort presents an alternative approach for the selection of the Gaussian kernel width via analysis of the distributional characteristics of the training data projected on the 'trained' SVM (margin values). The efficacy of a particular kernel width can be visually determined via one-dimensional density estimate plots of the training data margin values. Projecting the data onto the SVM hyperplane allows the one-dimensional analysis of the data from the viewpoint of the 'trained' SVM. The effect of kernel parameter selection on class-conditional margin distributions is demonstrated in the one-dimensional projection subspace, and a criterion for unsupervised optimization of kernel width is discussed. Empirical results are given for two classification problems: the 'toy' checkerboard problem and a high dimensional classification problem using simulated High-Resolution Radar (HRR) targets projected into a wavelet packet feature space.

Paper Details

Date Published: 16 September 2003
PDF: 10 pages
Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.487379
Show Author Affiliations
Donald Waagen, Raytheon Missile Systems (United States)
Mary Cassabaum, Raytheon Missile Systems (United States)
Harry A. Schmitt, Raytheon Missile Systems (United States)
Bruce Pollock, Raytheon Missile Systems (United States)


Published in SPIE Proceedings Vol. 5094:
Automatic Target Recognition XIII
Firooz A. Sadjadi, Editor(s)

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