Sequential minimal optimization

Results: 37



#Item
1Multiple Kernel Learning, Conic Duality, and the SMO Algorithm Francis R. Bach & Gert R. G. Lanckriet {fbach,gert}@cs.berkeley.edu Department of Electrical Engineering and Computer Science, University of California, Ber

Multiple Kernel Learning, Conic Duality, and the SMO Algorithm Francis R. Bach & Gert R. G. Lanckriet {fbach,gert}@cs.berkeley.edu Department of Electrical Engineering and Computer Science, University of California, Ber

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Source URL: eceweb.ucsd.edu

Language: English - Date: 2015-07-31 19:00:25
2Nonlinear Feature Selection with the Potential Support Vector Machine Sepp Hochreiter and Klaus Obermayer Technische Universit¨ at Berlin Fakult¨

Nonlinear Feature Selection with the Potential Support Vector Machine Sepp Hochreiter and Klaus Obermayer Technische Universit¨ at Berlin Fakult¨

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Source URL: www.bioinf.jku.at

Language: English - Date: 2013-01-23 02:44:45
3Chest Pain in the Emergency Department: Use of Asymmetric Penalties in Sequential Minimal Optimization with Feature Selection to Improve Clinical Decision Making Accuracy

Chest Pain in the Emergency Department: Use of Asymmetric Penalties in Sequential Minimal Optimization with Feature Selection to Improve Clinical Decision Making Accuracy

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Source URL: cs229.stanford.edu

Language: English - Date: 2011-09-14 20:33:05
    4Journal of Machine Learning Research–1619 Submitted 3/05; Published 9/05 Fast Kernel Classifiers with Online and Active Learning

    Journal of Machine Learning Research–1619 Submitted 3/05; Published 9/05 Fast Kernel Classifiers with Online and Active Learning

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    Source URL: www.jmlr.org

    Language: English - Date: 2005-09-27 11:30:43
    50 Large Linear Classification When Data Cannot Fit In Memory Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang and Chih-Jen Lin, Department of Computer Science, National Taiwan University Recent advances in linear classificati

    0 Large Linear Classification When Data Cannot Fit In Memory Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang and Chih-Jen Lin, Department of Computer Science, National Taiwan University Recent advances in linear classificati

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    Source URL: www.csie.ntu.edu.tw

    Language: English - Date: 2011-11-04 17:52:46
    6Kernel Methods Fast Algorithms and Real Life Applications A Thesis Submitted For the Degree of

    Kernel Methods Fast Algorithms and Real Life Applications A Thesis Submitted For the Degree of

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    Source URL: www.stat.purdue.edu

    Language: English - Date: 2008-09-11 13:00:43
    7Lower Bounds on Rate of Convergence of Cutting Plane Methods Xinhua Zhang Dept. of Computing Science University of Alberta

    Lower Bounds on Rate of Convergence of Cutting Plane Methods Xinhua Zhang Dept. of Computing Science University of Alberta

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    Source URL: people.cs.uchicago.edu

    Language: English - Date: 2010-11-03 13:50:24
    8Linear Support Vector Machines via Dual Cached Loops ∗ Shin Matsushima Information Science

    Linear Support Vector Machines via Dual Cached Loops ∗ Shin Matsushima Information Science

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    Source URL: www.stat.purdue.edu

    Language: English - Date: 2013-01-13 21:19:49
    9Linear Support Vector Machines via Dual Cached Loops ∗ Shin Matsushima Information Science

    Linear Support Vector Machines via Dual Cached Loops ∗ Shin Matsushima Information Science

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    Source URL: www.r.dl.itc.u-tokyo.ac.jp

    Language: English - Date: 2012-03-07 22:51:07
    10The Pennsylvania State University The Graduate School LEARNING IN EXTREME CONDITIONS: ONLINE AND ACTIVE LEARNING WITH MASSIVE, IMBALANCED AND NOISY DATA

    The Pennsylvania State University The Graduate School LEARNING IN EXTREME CONDITIONS: ONLINE AND ACTIVE LEARNING WITH MASSIVE, IMBALANCED AND NOISY DATA

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    Source URL: web.mit.edu

    Language: English - Date: 2010-06-04 20:53:56