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» Evaluating learning algorithms and classifiers
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AI
2010
Springer
14 years 11 months ago
Robustness of Classifiers to Changing Environments
Abstract. In this paper, we test some of the most commonly used classifiers to identify which ones are the most robust to changing environments. The environment may change over tim...
Houman Abbasian, Chris Drummond, Nathalie Japkowic...
COMAD
2008
15 years 6 months ago
REBMEC: Repeat Based Maximum Entropy Classifier for Biological Sequences
An important problem in biological data analysis is to predict the family of a newly discovered sequence like a protein or DNA sequence, using the collection of available sequence...
Pratibha Rani, Vikram Pudi
CDC
2009
IEEE
126views Control Systems» more  CDC 2009»
15 years 5 months ago
Support vector machine classifiers for sequential decision problems
Classification problems in critical applications such as health care or security often require very high reliability because of the high costs of errors. In order to achieve this r...
Eladio Rodriguez Diaz, David A. Castaon

Publication
222views
16 years 1 months ago
Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration
Abstract: Several approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervis...
Christos Dimitrakakis, Michail G. Lagoudakis
AAAI
2008
15 years 6 months ago
Constraint Projections for Ensemble Learning
It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods obtain diverse individual learners through res...
Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou, Qiang ...