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ICML
2008
IEEE
16 years 7 months ago
Optimizing estimated loss reduction for active sampling in rank learning
Learning to rank is becoming an increasingly popular research area in machine learning. The ranking problem aims to induce an ordering or preference relations among a set of insta...
Pinar Donmez, Jaime G. Carbonell
ICML
2004
IEEE
16 years 7 months ago
Learning and evaluating classifiers under sample selection bias
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model...
Bianca Zadrozny
ACML
2009
Springer
15 years 10 months ago
Max-margin Multiple-Instance Learning via Semidefinite Programming
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
Yuhong Guo
ICALT
2005
IEEE
16 years 10 days ago
Mining Learner Profile Utilizing Association Rule for Common Learning Misconception Diagnosis
With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning fields. Most past researches for we...
Chih-Ming Chen, Ying-Ling Hsieh
COLT
2006
Springer
15 years 10 months ago
Memory-Limited U-Shaped Learning
U-shaped learning is a learning behaviour in which the learner first learns a given target behaviour, then unlearns it and finally relearns it. Such a behaviour, observed by psych...
Lorenzo Carlucci, John Case, Sanjay Jain, Frank St...