Simplified Support Vector Decision Rules (1996) [98 citations — 4 self]
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author = {Chris J.C. Burges},
title = {Simplified Support Vector Decision Rules},
booktitle = {},
year = {1996},
pages = {71--77},
publisher = {Morgan Kaufmann}
}
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Abstract:
A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors, and by a set of corresponding weights. An SVM is also characterized by a kernel function. Choice of the kernel determines whether the resulting SVM is a polynomial classifier, a two-layer neural network, a radial basis function machine, or some other learning machine. SVMs are currently considerably slower in test phase than other approaches with similar generalization performance. To address this, we present a general method to significantly decrease the complexity of the decision rule obtained using an SVM. The proposed method computes an approximation to the decision rule in terms of a reduced set of vectors. These reduced set vectors are not support vectors and can in some cases be computed analytically. We give experimental results for three pattern recognition problems. The results show that the method can decrease the computational complexity of th...
Citations
| 5044 | Statistical Learning Theory – Vapnik - 1998 |
| 1091 | Support-vector network – Cortes, Vapnik - 1995 |
| 719 | A training algorithm for optimal margin classifiers – Boser, Guyon, et al. - 1992 |
| 624 | Estimation of Dependences Based on Empirical Data – Vapnik - 1982 |
| 145 | Extracting support data for a given task – Schölkopf, Burges, et al. - 1995 |
| 52 | Comparison of view-based object recognition algorithms using realistic 3d models – Blanz, Scholkopf, et al. - 1996 |
| 44 | Comparison of classifier methods: a case study in handwritten digit recognition – Bottou, Cortes, et al. - 1994 |
| 4 | Private communication – Weissbuch, Cun - 1992 |
