39 6 CONSISTENCY

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Date: 2008-05-07 02:46:19 Mathematics Convex analysis Econometrics Decision theory Fisher consistency Empirical risk minimization Hinge loss Loss function Convex function Statistics Statistical theory Machine learning		39 6 CONSISTENCY Add to Reading List Source URL: www.stat.osu.edu Download Document from Source Website File Size: 68,05 KB Share Document on Facebook

	Multiclass Boosting with Hinge Loss based on Output Coding Tianshi Gao Electrical Engineering Department, Stanford, CAUSA Daphne Koller Computer Science Department, Stanford, CAUSA DocID: 1uWCf - View Document
	Robust Truncated Hinge Loss Support Vector Machines Yichao W U and Yufeng L IU The support vector machine (SVM) has been widely applied for classification problems in both machine learning and statistics. Despite its pop DocID: 1uuGd - View Document
	Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs: Appendices A. Probabilistic Soft Logic users (i.e., users that are not top users). DocID: 1ssNc - View Document
	In this talk, I will introduce hinge-loss Markov random fields (HLMRFs), a new kind of probabilistic graphical model that supports scalable collective inference from richly structured data. HL-MRFs unify three different DocID: 1s6kb - View Document
	In this talk, I will introduce hinge-loss Markov random fields (HLMRFs), a new kind of probabilistic graphical model that supports scalable collective inference from richly structured data. HL-MRFs unify three different DocID: 1rXoh - View Document