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Convex optimization / Machine learning / Operations research / Information retrieval / Learning to rank / Gradient boosting / Lagrange multiplier / Supervised learning / Interior point method / Mathematical optimization / Numerical analysis / Mathematical analysis


Document Date: 2009-12-30 01:26:32

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New York City / New York / Allerton / Sunnyvale / /

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Neural Information Processing Systems / Lg / Cambridge University Press / MIT Press / ACM Press / Zhaohui Zheng Yahoo! Labs / Z. / /

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Australian National University / Stanford University / /

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real world search engines / search engines using clickthrough data / search engine data / efficient solution / learning to rank algorithm / efficient boosting algorithm / efficient brute force solution / web search / log-barrier interior point algorithm / line search / search engine / reference algorithms / /

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Cambridge University / Research School of Information Sciences and Engineering Permission / MIT / Stanford University / Australian National University / /

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F. Tsai / Gq / J. Wang / Shihao Ji / W. Xiong / T. Qin / H. Li / Y. Liu / F. Xia / T. Y. Liu / Alex Smola / W. Zhang / Tq / J. Xu / /

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Pentax K-x Digital Camera / IntervalRank / /

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IEEE Transactions on Information Theory / Machine Learning / Journal of Machine Learning Research / /

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Stanford University / /

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reference algorithms / three algorithms / resulting algorithm / 156 Algorithms / log-barrier interior point algorithm / boosting algorithm / competing algorithms / efficient boosting algorithm / Data Mining / search engine / machine learning / four algorithms / /

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