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ICML
2003
IEEE
16 years 4 months ago
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labelin...
Wee Sun Lee, Bing Liu
ICML
1996
IEEE
16 years 4 months ago
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian ne...
Kazuo J. Ezawa, Moninder Singh, Steven W. Norton
CIKM
2009
Springer
15 years 10 months ago
Large margin transductive transfer learning
Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributi...
Brian Quanz, Jun Huan
ICML
2004
IEEE
15 years 9 months ago
Online learning of conditionally I.I.D. data
In this work we consider the task of relaxing the i.i.d assumption in online pattern recognition (or classification), aiming to make existing learning algorithms applicable to a ...
Daniil Ryabko
ECML
1998
Springer
15 years 7 months ago
Predicate Invention and Learning from Positive Examples Only
Previous bias shift approaches to predicate invention are not applicable to learning from positive examples only, if a complete hypothesis can be found in the given language, as ne...
Henrik Boström