Support Vector Machines (SVM; サポートベクタマシン)
に関するメモ
(論文のリストなど)。
Papers on Support Vector Machines (SVMs)
Bernhard Schoelkopf, John C. Platt,
John Shawe-Taylor, Alex J. Smolax, and Robert C. Williamson.
1999.
Estimating the Support of a High-Dimensional Distribution.
[pdf]
John C. Platt.
1999.
Using Analytic QP and Sparseness to Speed Training of Support Vector Machines.
[ps]
Dennis DeCoste and Kiri Wagstaff.
2000.
Alpha seeding for Support Vector Machines.
[ps]
Ralf Herbrich and Thore Graepel.
2000.
A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work.
NIPS 2000.
[ps.gz]
Larry M. Manevitz and Malik Yousef.
2001.
One-Class SVMs for Document Classification.
[pdf]
Cho-Jui Hsieh,
Kai-Wei Chang,
Chih-Jen Lin,
S. Sathiya Keerthi,
and S. Sundararajan.
2008.
A Dual Coordinate Descent Method for Large-scale Linear SVM.
[pdf]
Christopher J.C. Burges.
1998.
A Tutorial on Support Vector Machines for Pattern Recognition.
[pdf]
Nello Cristianini.
2001.
Support Vector and Kernel Machines.
[pdf]
Books on Support Vector Machines
サポートベクタマシン (SVM) やカーネル法 (kernel methods) に関する書籍。
Advances in Kernel Methods: Support Vector Learning
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The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition,
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Advances in Large-Margin Classifiers
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The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines
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