# Support vector machines: hype or hallelujah?

@article{Bennett2000SupportVM, title={Support vector machines: hype or hallelujah?}, author={Kristin P. Bennett and Colin Campbell}, journal={SIGKDD Explor.}, year={2000}, volume={2}, pages={1-13} }

Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective. The classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective. While this overview is not comprehensive, it does provide…

## 721 Citations

### Support vector machines for classification: a statistical portrait.

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This chapter aims to provide an introduction to the support vector machine, covering from the basic concept of the optimal separating hyperplane to its nonlinear generalization through kernels.

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Far from being a panacea, SVMs yet represent a powerful technique for general (nonlinear) classification , regression and outlier detection with an intuitive model representation.

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In the support Vector Machines classification technique the best possible discriminating hyperplane between two populations is looked for by maximizing of margin between the populations’ closest points, thus obtaining a nonlinear Support Vector Machines method.

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### Statistical Learning Theory and Support Vector Machines

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This paper overviews the pattern recognition techniques and describes the state of art in SVM in the field of pattern recognition.

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