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» Evaluating Adaptive Problem Selection
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INTERSPEECH
2010
14 years 8 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»
15 years 3 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»
14 years 8 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
15 years 3 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
15 years 5 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