16 September 2003 Support vector machine optimization via margin distribution analysis
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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.
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Donald Waagen, Donald Waagen, Mary Cassabaum, Mary Cassabaum, Harry A. Schmitt, Harry A. Schmitt, Bruce Pollock, Bruce Pollock, } "Support vector machine optimization via margin distribution analysis", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.487379; https://doi.org/10.1117/12.487379

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