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IPM
2008
100views more  IPM 2008»
13 years 4 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...
CORR
2006
Springer
109views Education» more  CORR 2006»
13 years 4 months ago
Decision Making with Side Information and Unbounded Loss Functions
We consider the problem of decision-making with side information and unbounded loss functions. Inspired by probably approximately correct learning model, we use a slightly differe...
Majid Fozunbal, Ton Kalker
CORR
2010
Springer
116views Education» more  CORR 2010»
13 years 4 months ago
Adaptive Bound Optimization for Online Convex Optimization
We introduce a new online convex optimization algorithm that adaptively chooses its regularization function based on the loss functions observed so far. This is in contrast to pre...
H. Brendan McMahan, Matthew J. Streeter
NAACL
2004
13 years 5 months ago
Minimum Bayes-Risk Decoding for Statistical Machine Translation
We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functio...
Shankar Kumar, William J. Byrne
NIPS
2008
13 years 6 months ago
Support Vector Machines with a Reject Option
We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow�...
Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshe...
ECCV
2010
Springer
13 years 10 months ago
Optimizing Complex Loss Functions in Structured Prediction
Abstract. In this paper we develop an algorithm for structured prediction that optimizes against complex performance measures, those which are a function of false positive and fals...
COLT
2004
Springer
13 years 10 months ago
Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results
One of the nice properties of kernel classifiers such as SVMs is that they often produce sparse solutions. However, the decision functions of these classifiers cannot always be u...
Peter L. Bartlett, Ambuj Tewari
RSKT
2009
Springer
13 years 11 months ago
Learning Optimal Parameters in Decision-Theoretic Rough Sets
A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented. The parameters can be used to define approximation regions in a p...
Joseph P. Herbert, Jingtao Yao
ALT
2000
Springer
14 years 1 months ago
On the Noise Model of Support Vector Machines Regression
Abstract. Support Vector Machines Regression (SVMR) is a learning technique where the goodness of fit is measured not by the usual quadratic loss function (the mean square error),...
Massimiliano Pontil, Sayan Mukherjee, Federico Gir...
ICML
2008
IEEE
14 years 5 months ago
Boosting with incomplete information
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we pres...
Feng Jiao, Gholamreza Haffari, Greg Mori, Shaojun ...