51![STAT 538 Lecture 2 Conjugate Function, Bregman Divergences, Concepts of Information Theory c
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52![Loss Functions Robert C. Williamson Abstract Vapnik described the “three main learning problems” of pattern recognition, regression estimation and density estimation. These are defined in terms of the loss functions Loss Functions Robert C. Williamson Abstract Vapnik described the “three main learning problems” of pattern recognition, regression estimation and density estimation. These are defined in terms of the loss functions](https://www.pdfsearch.io/img/f36bcbae5c458f988608ec7dd4089289.jpg) | Add to Reading ListSource URL: users.cecs.anu.edu.auLanguage: English - Date: 2013-04-06 02:21:38
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53![Convex Relaxations of Bregman Divergence Clustering Hao Cheng Department of Computing Science Xinhua Zhang Convex Relaxations of Bregman Divergence Clustering Hao Cheng Department of Computing Science Xinhua Zhang](https://www.pdfsearch.io/img/0bee2d0f24540c4c113129dfbaf9e76a.jpg) | Add to Reading ListSource URL: users.cecs.anu.edu.auLanguage: English - Date: 2013-07-06 17:47:25
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54![Journal of Machine Learning Research ? ([removed]Submitted 8/09; Published ??/?? Information, Divergence and Risk for Binary Experiments Mark D. Reid Journal of Machine Learning Research ? ([removed]Submitted 8/09; Published ??/?? Information, Divergence and Risk for Binary Experiments Mark D. Reid](https://www.pdfsearch.io/img/19fcb7b5347859625f0c32fcd5dc74ba.jpg) | Add to Reading ListSource URL: users.cecs.anu.edu.auLanguage: English - Date: 2009-08-23 23:03:10
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55![Accelerated Training of Max-Margin Markov Networks with Kernels Xinhua Zhang∗ Department of Computing Science, University of Alberta, Edmonton, AB T6G2E8, Canada Ankan Saha Accelerated Training of Max-Margin Markov Networks with Kernels Xinhua Zhang∗ Department of Computing Science, University of Alberta, Edmonton, AB T6G2E8, Canada Ankan Saha](https://www.pdfsearch.io/img/779c6aed96c2c62bdc6066e7f1ebca2d.jpg) | Add to Reading ListSource URL: users.cecs.anu.edu.auLanguage: English - Date: 2013-02-25 19:29:17
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56![Bregman Divergence and Mirror Descent 1 Bregman Divergence Bregman Divergence and Mirror Descent 1 Bregman Divergence](https://www.pdfsearch.io/img/beba9ed53ff03c6f07ff8aad70b876e4.jpg) | Add to Reading ListSource URL: users.cecs.anu.edu.auLanguage: English - Date: 2013-12-27 03:32:34
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57![Regularizers versus Losses for Nonlinear Dimensionality Reduction Regularizers versus Losses for Nonlinear Dimensionality Reduction](https://www.pdfsearch.io/img/3d338f10fbda64dbf7a5a1231e4ea35e.jpg) | Add to Reading ListSource URL: users.cecs.anu.edu.auLanguage: English - Date: 2012-06-02 22:35:08
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58![PQBSBKMFKHBOLK ALBERT BREGMAN Albert Bregman, my mentor in cognitive psychology, was hardly my role model as a teacher. I became a modern lecturer-entertainer, with bullet points, borscht-belt humor and audiovisual ra PQBSBKMFKHBOLK ALBERT BREGMAN Albert Bregman, my mentor in cognitive psychology, was hardly my role model as a teacher. I became a modern lecturer-entertainer, with bullet points, borscht-belt humor and audiovisual ra](https://www.pdfsearch.io/img/c488521ad01a0bbe0f0e51103d3b08d9.jpg) | Add to Reading ListSource URL: pinker.wjh.harvard.eduLanguage: English - Date: 2011-04-07 12:18:08
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59![JMLR: Workshop and Conference Proceedings vol[removed]–[removed]25th Annual Conference on Learning Theory A Characterization of Scoring Rules for Linear Properties JABER @ SEAS . UPENN . EDU ∗ JMLR: Workshop and Conference Proceedings vol[removed]–[removed]25th Annual Conference on Learning Theory A Characterization of Scoring Rules for Linear Properties JABER @ SEAS . UPENN . EDU ∗](https://www.pdfsearch.io/img/2008a8f44430f3a22e3548aecf751117.jpg) | Add to Reading ListSource URL: jmlr.csail.mit.eduLanguage: English - Date: 2012-06-17 06:50:53
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60![Strictly Proper Scoring Rules, Prediction, and Estimation Tilmann G NEITING and Adrian E. R AFTERY Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distr Strictly Proper Scoring Rules, Prediction, and Estimation Tilmann G NEITING and Adrian E. R AFTERY Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distr](https://www.pdfsearch.io/img/f24664ec886c81ed5d5341db5b3b90df.jpg) | Add to Reading ListSource URL: www.csss.washington.eduLanguage: English - Date: 2008-05-20 22:04:51
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