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ç§©çˆ¶é‰„é“ @mine_o 僕ã¯ç§å¤§æ–‡ç³»ãªã®ã«æ—¥æœ¬å²ã‚‚世界å²ã‚‚ã‚„ã£ã¦ãªã„ゴミã ã‹ã‚‰ã€æ´å²ã®æ•™é¤Šã¨ã‹ã®è©±ã«ãªã‚‹ã¨ãŸã¶ã‚“ã‚‚ã®ã™ã”ã„æ¥ãšã‹ã—ã„ã“ã¨ã«ãªã‚‹ã€‚ã§ã€ã„ã¾ã•ã‚‰å‹‰å¼·ã—ã¦ã„ã‚‹ã‘ã©ã“ã†ã„ã†ã®ã£ã¦ã‚¢ã‚¹ãƒšãƒ«ã‚¬ãƒ¼åŠ›ãŒãƒžãƒƒã‚¯ã‚¹ã ã£ãŸä¸é«˜ç”Ÿã®ã¨ãã«ã‚„ã‚‹ã¹ãã よãªã‚。人生失敗ã—ãŸãªã‚ 2010-11-17 10:29:06
Dr. J. Liu, Harvard, Tutorial: Gibbs Sampler and MCMC Methods
To begin viewing slides, click on the first slide below. Author entry (protected)
SELECT Lab::Tutorial at ICML 2008: Beyond Convexity - Submodularity in Machine Learning
Beyond Convexity: Submodularity in Machine Learning Description Convex optimization has become a main workhorse for many machine learning algorithms during the past ten years. When minimizing a convex loss function for, e.g., training a Support Vector Machine, we can rest assured to efficiently find an optimal solution, even for large problems. In recent years, another fundamental problem st
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Active Learning with Statistical Models
Simon Tong's Research page
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