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… 

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References

SHOWING 1-10 OF 106 REFERENCES

Support Vector Regression Machines

This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.

Support vector machines for spam categorization

The use of support vector machines in classifying e-mail as spam or nonspam is studied by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees, which found SVM's performed best when using binary features.

Text Categorization with Support Vector Machines: Learning with Many Relevant Features

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are

Reducing the run-time complexity of Support Vector Machines

This paper presents two relevant results: a) the use of SVM itself as a regression tool to approximate the decision surface with a user-speciied accuracy; and b) a reformulation of the training problem that yields the exact same decision surface using a smaller number of basis functions.

Bounds on Error Expectation for Support Vector Machines

It is proved that the value of the span is always smaller (and can be much smaller) than the diameter of the smallest sphere containing the support vectors, used in previous bounds.

Advances in kernel methods: support vector learning

Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.

New Support Vector Algorithms

A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.

Advances in Large Margin Classifiers

This book provides an overview of recent developments in large margin classifiers, examines connections with other methods, and identifies strengths and weaknesses of the method, as well as directions for future research.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.

The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines

This paper proposes an adaptation of the Adatron algorithm for clas-siication with kernels in high dimensional spaces that can find a solution very rapidly with an exponentially fast rate of convergence towards the optimal solution.
...