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Machine learning / Mathematical optimization / Hinge loss / Convex function / Convex optimization / Perceptron / Support vector machine / Loss function / Linear prediction / Statistics / Statistical classification / Convex analysis


Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss Shai Ben-David University of Waterloo David Loker
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Document Date: 2012-06-07 13:20:54

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City

New York / Edinburgh / /

Company

Neural Information Processing Systems / MIT Press / ACM Press / /

Country

Jordan / United States / United Kingdom / Scotland / /

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Facility

David University of Waterloo David Loker University / Nathan Srebro Toyota Technological Institute / The Hebrew University / Chicago Karthik Sridharan University of Pennsylvania Abstract We / /

IndustryTerm

isotron algorithm / loss minimization algorithm / conservative update algorithm / proper learning algorithm / Online Learning / stochastic gradient descent algorithms / randomized improper learning algorithm / learning algorithms / /

Organization

American Statistical Association / MIT / Hebrew University / University of Waterloo / Chicago Karthik Sridharan University of Pennsylvania Abstract We / /

Person

Rocco A. Agnostically / Kalai / Hans Ulrich / Rocco A. Random / Rocco A. Learning / Mansour / Adam Tauman / Hypothesis Classes / /

Position

author / General / /

ProgrammingLanguage

R / /

ProvinceOrState

New York / /

PublishedMedium

Machine Learning / Journal of the American Statistical Association / /

Technology

stochastic gradient descent algorithms / randomized improper learning algorithm / machine learning / conservative update algorithm / R. The isotron algorithm / proper learning algorithm / loss minimization algorithm / /

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