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Loss function
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171	Active Learning for Logistic Regression Andrew I. Schein The University of Pennsylvania Department of Computer and Information Science Philadelphia, PAUSA Add to Reading List Source URL: www.andrewschein.com Language: English - Date: 2011-08-13 15:18:52 Machine learning Logistic regression Multinomial logit Mean squared error Training set Loss function Statistical classification Supervised learning Book:Machine Learning - The Complete Guide Statistics Econometrics Regression analysis
172	A-Optimality for Active Learning of Logistic Regression Classifiers∗ Andrew I. Schein and Lyle H. Ungar Department of Computer and Information Science Levine Hall, 3330 Walnut Street Philadelphia, PA Add to Reading List Source URL: www.andrewschein.com Language: English - Date: 2011-08-13 15:16:54 Econometrics Statistical theory Statistical methods Optimal design Linear regression Supervised learning Loss function Regularization Statistical classification Statistics Regression analysis Machine learning
173	Taxonomic Multi-class Prediction and Person Layout using Efficient Structured Ranking Arpit Mittal1 , Matthew B. Blaschko2 , Andrew Zisserman1 , and Philip H. S. Torr3 1 2 Add to Reading List Source URL: www.robots.ox.ac.uk Language: English - Date: 2012-08-06 03:10:26 Statistical classification Structured SVM Ranking SVM Structured prediction Regression analysis Loss function Statistics Support vector machines Econometrics
174	Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information ∗ Christiane Baumeister Add to Reading List Source URL: econweb.ucsd.edu Language: English - Date: 2015-05-16 10:46:19 Prior probability Conjugate prior Loss function Vector autoregression Bayesian linear regression Statistics Bayesian statistics Bayesian inference
175	The Interplay Between Stability and Regret in Online Learning Ankan Saha Department of Computer Science University of Chicago Add to Reading List Source URL: people.cs.uchicago.edu Language: English - Date: 2012-12-03 21:04:12 Statistics Stability Lipschitz continuity Convex function Loss function Mathematical optimization Linear programming Continuous function Operations research Mathematical analysis Mathematics
176	Gaussian Process Preference Elicitation Edwin V. Bonilla, Shengbo Guo, Scott Sanner NICTA & ANU, Locked Bag 8001, Canberra ACT 2601, Australia {edwin.bonilla, shengbo.guo, scott.sanner}@nicta.com.au Abstract Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2010-11-05 21:14:04 Statistical theory Econometrics Information systems Normal distribution Preference elicitation Loss function Collaborative filtering Likelihood function Gaussian process Statistics Estimation theory Bayesian statistics
177	MODIFIED MPE/MMI IN A TRANSDUCER-BASED FRAMEWORK G. Heigold, R. Schl¨uter, and H. Ney Chair of Computer Science 6 - Computer Science Department RWTH Aachen University, Aachen {heigold,schlueter,ney}@cs.rwth-aachen.de AB Add to Reading List Source URL: wiki.inf.ed.ac.uk Language: English - Date: 2009-06-30 07:12:36 Computer accessibility Speech recognition Support vector machine Finite state transducer Margin classifier Hinge loss Mathematical optimization Loss function Statistics Statistical classification Automatic identification and data capture
178	Empirical Minimum Bayes Risk Prediction: How to extract an extra few% performance from vision models with just three more parameters Vittal Premachandran* National University of Singapore Add to Reading List Source URL: filebox.ece.vt.edu Language: English - Date: 2014-04-22 19:35:34 Estimation theory Bayesian inference Statistical theory Empirical risk minimization Supervised learning Loss function Bayes estimator Regression analysis Image Statistics Econometrics Machine learning
179	RIGHT CENSORED RAYLEIGH MODEL: SHRINKAGE AND RELIABILITY ESTIMATORS* J.T. FERREIRA†, A. BEKKER & J.J.J. ROUX Department of Statistics, University of Pretoria, email: M. ARASHI Add to Reading List Source URL: www.statistics.gov.hk Language: English - Date: 2013-08-22 04:39:12 Statistical inference Point estimation Estimator Bayes estimator Loss function Mean squared error Shrinkage estimator Efficient estimator Minimax estimator Statistics Estimation theory Statistical theory
180	Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications Andreas Buja 1 Add to Reading List Source URL: stat.wharton.upenn.edu Language: English - Date: 2005-11-03 13:35:44 Regression analysis Statistical models Econometrics Scoring rule Kullback–Leibler divergence Generalized linear model Bregman divergence Loss function Divergence Statistics Decision theory Statistical theory