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

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Beijing / /

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Cambridge University Press / Neural Networks / MIT Press / Yahoo! / /

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China / /

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AMD / CUP / USD / /

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Technology The University of Tokyo / /

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distributed online prediction using mini-batches / Implicit online learning / individual processors / machine learning algorithms / optimization algorithm / Message-passing algorithms / manycore processors / online version / accuracy solution / final solution / Parallel software / stochastic gradient descent solutions / multiprocessor systems / computer hardware / similar solutions / passive-aggressive algorithms / real-world applications / purpose hardware / vector networks / optimization algorithms / storage systems / intermediate solutions / online and active learning / mining / dual descent algorithms / convex optimization tools / computing / tion algorithm / pre-processing / primal solution / /

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Pegasos / POSIX / /

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Cambridge University / MIT / Kyoto Cabinet / UC Berkeley / Department of Statistics / University of Tokyo / Tokyo / /

Person

Joseph Keshet / N. Srebro / S. Sonnenburg / V / Shai Shalev-Shwartz / Ofer Dekel / Alexander J. Smola / Y. Singer / S. Shalev-Shwartz / Shin Matsushima / /

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queue manager / design and evaluation General / /

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R / C++ / /

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Machine Learning / /

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1.4 Wall / /

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RAM / machine learning algorithms / following algorithm / machine learning / random access / optimizaData Intermediate Set Working Set Para tion algorithm / html / optimization algorithm / Message-passing algorithms / pdf / LMS algorithm / neural network / manycore processors / 6172 processors / dual descent algorithms / optimization algorithms / Online passive-aggressive algorithms / caching / /

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