暂无分享,去创建一个
[1] Hans Ulrich Simon,et al. Robust Trainability of Single Neurons , 1995, J. Comput. Syst. Sci..
[2] Philip M. Long,et al. Consistency versus Realizable H-Consistency for Multiclass Classification , 2013, ICML.
[3] Yi Lin. A note on margin-based loss functions in classification , 2004 .
[4] Peter L. Bartlett,et al. Boosting Algorithms as Gradient Descent , 1999, NIPS.
[5] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[6] Yufeng Liu,et al. Fisher Consistency of Multicategory Support Vector Machines , 2007, AISTATS.
[7] Scott Sanner,et al. Algorithms for Direct 0-1 Loss Optimization in Binary Classification , 2013, ICML.
[8] Jason Weston,et al. Multi-Class Support Vector Machines , 1998 .
[9] Patrick Gallinari,et al. Calibration and regret bounds for order-preserving surrogate losses in learning to rank , 2013, Machine Learning.
[10] Shai Ben-David,et al. On the difficulty of approximately maximizing agreements , 2000, J. Comput. Syst. Sci..
[11] Ambuj Tewari,et al. Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses , 2013, NIPS.
[12] Frank Nielsen,et al. Bregman Divergences and Surrogates for Learning , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Ingo Steinwart. How to Compare Different Loss Functions and Their Risks , 2007 .
[14] Christian Igel,et al. A Unified View on Multi-class Support Vector Classification , 2016, J. Mach. Learn. Res..
[15] David J. Kriegman,et al. Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting , 2014, ICML.
[16] H. Zou. The Margin Vector , Admissible Loss and Multi-class Margin-based Classifiers , 2005 .
[17] Tao Sun,et al. Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss , 2006, J. Mach. Learn. Res..
[18] S. Boucheron,et al. Theory of classification : a survey of some recent advances , 2005 .
[19] A. Raftery,et al. Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .
[20] Yi Lin. Multicategory Support Vector Machines, Theory, and Application to the Classification of . . . , 2003 .
[21] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[22] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[23] Zhenhua Wang,et al. A Hybrid Loss for Multiclass and Structured Prediction , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Tong Zhang,et al. Statistical Analysis of Some Multi-Category Large Margin Classification Methods , 2004, J. Mach. Learn. Res..
[25] Koby Crammer,et al. Ultraconservative Online Algorithms for Multiclass Problems , 2001, J. Mach. Learn. Res..
[26] Prasad Raghavendra,et al. Agnostic Learning of Monomials by Halfspaces Is Hard , 2009, 2009 50th Annual IEEE Symposium on Foundations of Computer Science.
[27] Shivani Agarwal,et al. Convex Calibration Dimension for Multiclass Loss Matrices , 2014, J. Mach. Learn. Res..
[28] V. Koltchinskii,et al. Oracle inequalities in empirical risk minimization and sparse recovery problems , 2011 .
[29] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[30] Mark D. Reid,et al. Surrogate regret bounds for proper losses , 2009, ICML '09.
[31] Mark D. Reid,et al. Composite Binary Losses , 2009, J. Mach. Learn. Res..
[32] Ambuj Tewari,et al. On the Consistency of Multiclass Classification Methods , 2007, J. Mach. Learn. Res..
[33] Shivani Agarwal,et al. Classification Calibration Dimension for General Multiclass Losses , 2012, NIPS.
[34] Csaba Szepesvári,et al. Cost-sensitive Multiclass Classification Risk Bounds , 2013, ICML.
[35] Michael I. Jordan,et al. Convexity, Classification, and Risk Bounds , 2006 .
[36] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[37] Lorenzo Rosasco,et al. Multiclass Learning with Simplex Coding , 2012, NIPS.
[38] Ryan M. Rifkin,et al. In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..
[39] E. Mammen,et al. Smooth Discrimination Analysis , 1999 .
[40] Klaus Nordhausen,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .