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}
}
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References

SHOWING 1-10 OF 105 REFERENCES
Support Vector Regression Machines
TLDR
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. Expand
Support vector machines for spam categorization
TLDR
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. Expand
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 areExpand
Reducing the run-time complexity of Support Vector Machines
TLDR
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. Expand
Bounds on Error Expectation for Support Vector Machines
TLDR
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. Expand
New Support Vector Algorithms
TLDR
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. Expand
Advances in Large Margin Classifiers
TLDR
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. Expand
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TLDR
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. Expand
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
TLDR
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. Expand
Comparing support vector machines with Gaussian kernels to radial basis function classifiers
TLDR
The results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system, and the SV approach is thus not only theoretically well-founded but also superior in a practical application. Expand
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