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ICML
2007
IEEE
10 years 10 months ago
Discriminative learning for differing training and test distributions
We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution--problems also ...
Michael Brückner, Steffen Bickel, Tobias Sche...
ICML
2007
IEEE
10 years 10 months ago
Asymptotic Bayesian generalization error when training and test distributions are different
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
Keisuke Yamazaki, Klaus-Robert Müller, Masash...
PAKDD
2005
ACM
128views Data Mining» more  PAKDD 2005»
10 years 3 months ago
A Framework for Incorporating Class Priors into Discriminative Classification
Abstract. Discriminative and generative methods provide two distinct approaches to machine learning classification. One advantage of generative approaches is that they naturally mo...
Rong Jin, Yi Liu
EMNLP
2006
9 years 11 months ago
Domain Adaptation with Structural Correspondence Learning
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For...
John Blitzer, Ryan T. McDonald, Fernando Pereira
ICPR
2004
IEEE
10 years 10 months ago
Discriminative Distance Measures for Image Matching
: Significant progress has been made by the computer vision community in recent years along two fronts: (i) developing complex spatial-temporal models for object registration and t...
Tat-Jen Cham, Xi Chen
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