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Mathematics / Support vector machine / Supervised learning / Structural risk minimization / Transduction / Cross-validation / Mathematical optimization / Vector space / Quadratic programming / Statistics / Algebra / Machine learning


Semi-Supervised Support Vector Machines Kristin P. Bennett∗ Department of Mathematical Sciences Rensselaer Polytechnic Institute Troy, NY[removed]removed]
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Document Date: 2003-01-15 10:44:57

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City

Denver / Washington / D.C. / Moscow / Springer Verlag / Danvers / Troy / Springer / /

Company

P. Venugopal L.P. / Artificial Neural Networks / CPLEX Optimization Incorporated / Microsoft / /

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Facility

University of WisconsinMadison / L.O. Hall / University of Sheffield / Maassachusets Institute of Technology / CPLEX Callable Library / University of California / University of Wisconsin-Madison / /

IndustryTerm

vector networks / semi-supervised algorithms / purpose algorithms / neural networks / computational solutions / sr algorithm / /

Organization

Republican Party / University of WisconsinMadison / Semi-Supervised Support Vector Machines Kristin P. Bennett∗ Department of Mathematical Sciences Rensselaer Polytechnic Institute Troy / University of Sheffield / Rensselaer Polytechnic Institute / Department of Information and Computer Science / Institute of Technology / Mathematical Association of America / Ayhan Demiriz Department of Decision Sciences and Engineering Systems Rensselaer Polytechnic Institute Troy / University of California / Irvine / University of Wisconsin / /

Person

Geometry / Harrison Fries / Kristin P. Bennett / /

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Position

Major / standard SVM model for structural risk minimization / /

ProgrammingLanguage

AMPL / /

ProvinceOrState

Wisconsin / Nevada / California / New York / Massachusetts / /

PublishedMedium

Astronomical Journal / Machine Learning / /

Technology

sr algorithm / S3VM algorithms / semi-supervised algorithms / Data Mining / Machine Learning / purpose algorithms / mri / /

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