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Looking for sites in Computers, Artificial Intelligence, Support Vector Machines, Publications ? ActiPages covers a large variety of annotated web sites related to Computers, Artificial Intelligence, Support Vector Machines, Publications. This category lists wide range of carefully selected web sites in Computers, Artificial Intelligence, Support Vector Machines, Publications.

http://research.nii.ac.jp/~aihara/images/aihara-sci2000.pdf

» Customization using Support Vector Machines for Information Retrieval - Paper by Kenro Aihara and Atsuhiro Takasu, which proposes an HCI approach to supporting interactive document retrieval in unorganized open information space. 2000. [PDF]

http://www.jmlr.org/papers/volume3/bi03a/bi03a.pdf

» Dimensionality Reduction via Sparse Support Vector Machines - Appears in Journal of Machine Learning Research. 2003. [PDF]

http://www.cs.brown.edu/people/th/papers/AltTsoHof-ICML2003.pdf

» Hidden Markov Support Vector Machines - Appears in Proceedings of the 20th International Conference on Machine Learning (ICML 2003). [PDF]

http://epubs.siam.org/sam-bin/dbq/article/37437

» Interior-Point Methods for Massive Support Vector Machines - SIAM Journal on Optimization, Vol. 13, Iss. 3. Several formats of document available. [subscription required]

» An Introduction to Support Vector Machines - An introductory book to the field of Support Vector Machines, a novel machine learning algorithm.

http://www1.cs.columbia.edu/~dplewis/candidacy/bennett98semisupervised.pdf

» Semi-Supervised Support Vector - We introduce a semi-supervised support vector machine (S3VM) method. [PDF]

http://www.computer.org/intelligent/ex1998/pdf/x4018.pdf

» Support Vector Machines - "IEEE Intelligent Systems" article by Marti Hearst. July/August 1998. [PDF]

http://citeseer.ist.psu.edu/burges98tutorial.html

» A Tutorial on Support Vector Machines for Pattern Recognition - By Christopher Burges. 1998. Several formats of document available.

http://www.csie.ntu.edu.tw/~cjlin/papers/nusvmtutorial.pdf

» A Tutorial on v-Support Vector Machines - Paper by Pai-Hsuen Chen, Chih-Jen Lin, and Bernhard Schölkopf, describing the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. [PDF]

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