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» Evaluating Adaptive Problem Selection
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INTERSPEECH
2010
13 years 3 months ago
Regularized-MLLR speaker adaptation for computer-assisted language learning system
In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assisted Language Learning (CALL) systems. This method uses a linear combination of ...
Dean Luo, Yu Qiao, Nobuaki Minematsu, Yutaka Yamau...
MC
2008
122views Computer Science» more  MC 2008»
13 years 10 months ago
Interaction Techniques for Case Selection in Medical Computer Based Training Systems
The vast majority of medical computer-based training (CBT) systems aim at problem-oriented case based training. A crucial issue in the design of CBT systems is the selection of ap...
Ragnar Bade, Jeanette Cordes, Maik Mewes, Bernhard...
TNN
2010
154views Management» more  TNN 2010»
13 years 3 months ago
Discriminative semi-supervised feature selection via manifold regularization
We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number ...
Zenglin Xu, Irwin King, Michael R. Lyu, Rong Jin
CPAIOR
2008
Springer
13 years 10 months ago
The Accuracy of Search Heuristics: An Empirical Study on Knapsack Problems
Theoretical models for the evaluation of quickly improving search strategies, like limited discrepancy search, are based on specific assumptions regarding the probability that a va...
Daniel H. Leventhal, Meinolf Sellmann
ICPR
2010
IEEE
13 years 12 months ago
Feature Selection Using Multiobjective Optimization for Named Entity Recognition
Appropriate feature selection is a very crucial issue in any machine learning framework, specially in Maximum Entropy (ME). In this paper, the selection of appropriate features for...
Asif Ekbal, Sriparna Saha