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» Evaluating learning algorithms and classifiers
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ICASSP
2011
IEEE
13 years 2 months ago
Similarity learning for semi-supervised multi-class boosting
In semi-supervised classification boosting, a similarity measure is demanded in order to measure the distance between samples (both labeled and unlabeled). However, most of the e...
Q. Y. Wang, Pong Chi Yuen, Guo-Can Feng
AUSAI
2001
Springer
14 years 2 months ago
Fast Text Classification Using Sequential Sampling Processes
A central problem in information retrieval is the automated classification of text documents. While many existing methods achieve good levels of performance, they generally require...
Michael D. Lee
GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
14 years 3 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...
BMCBI
2010
182views more  BMCBI 2010»
13 years 11 months ago
L2-norm multiple kernel learning and its application to biomedical data fusion
Background: This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields diff...
Shi Yu, Tillmann Falck, Anneleen Daemen, Lé...
AIIDE
2006
14 years 5 days ago
The Self Organization of Context for Learning in MultiAgent Games
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
Christopher D. White, Dave Brogan