文件名称:etr20652
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In this letter, we propose a new compression method for a
high dimensional support vector machine (SVM). We used
singular value decomposition (SVD) to compress the norm part
of a radial basis function SVM. By deleting the least significant
vectors that are extracted from the decomposition, we can
compress each vector with minimized energy loss. We select the
compressed vector dimension according to the predefined
threshold which can limit the energy loss to design criteria
high dimensional support vector machine (SVM). We used
singular value decomposition (SVD) to compress the norm part
of a radial basis function SVM. By deleting the least significant
vectors that are extracted from the decomposition, we can
compress each vector with minimized energy loss. We select the
compressed vector dimension according to the predefined
threshold which can limit the energy loss to design criteria
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