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» Evaluating learning algorithms and classifiers
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ICML
2006
IEEE
15 years 10 months ago
Locally adaptive classification piloted by uncertainty
Locally adaptive classifiers are usually superior to the use of a single global classifier. However, there are two major problems in designing locally adaptive classifiers. First,...
Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwo...
GECCO
2007
Springer
194views Optimization» more  GECCO 2007»
15 years 3 months ago
Hybrid coevolutionary algorithms vs. SVM algorithms
As a learning method support vector machine is regarded as one of the best classifiers with a strong mathematical foundation. On the other hand, evolutionary computational techniq...
Rui Li, Bir Bhanu, Krzysztof Krawiec
INTERSPEECH
2010
14 years 4 months ago
Data-dependent evaluator modeling and its application to emotional valence classification from speech
Practical supervised learning scenarios involving subjectively evaluated data have multiple evaluators, each giving their noisy version of the hidden ground truth. Majority logic ...
Kartik Audhkhasi, Shrikanth S. Narayanan
78
Voted
ICALT
2005
IEEE
15 years 3 months ago
The Effect of Correlation on the Accuracy of Meta-Learning Approach
Meta-learning is an efficient approach in the field of machine learning, which involves multiple classifiers. In this paper, a meta-learning framework consisting of stacking meta-...
Li-ying Yang, Zheng Qin
ICML
2000
IEEE
15 years 10 months ago
Incremental Learning in SwiftFile
SwiftFile is an intelligent assistant that helps users organize their e-mail into folders. SwiftFile uses a text classifier to predict where each new message is likely to be filed...
Richard Segal, Jeffrey O. Kephart