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JMLR
2006
156views more  JMLR 2006»
15 years 2 months ago
Large Scale Multiple Kernel Learning
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic ...
Sören Sonnenburg, Gunnar Rätsch, Christi...
146
Voted
PAMI
2011
14 years 9 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
100
Voted
CORR
2006
Springer
178views Education» more  CORR 2006»
15 years 2 months ago
Low-rank matrix factorization with attributes
We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classi...
Jacob Abernethy, Francis Bach, Theodoros Evgeniou,...
155
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ESANN
2007
15 years 4 months ago
How to process uncertainty in machine learning?
Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
Barbara Hammer, Thomas Villmann
AAMAS
2002
Springer
15 years 2 months ago
Multiagent Learning for Open Systems: A Study in Opponent Classification
Abstract. Open systems are becoming increasingly important in a variety of distributed, networked computer applications. Their characteristics, such as agent diversity, heterogenei...
Michael Rovatsos, Gerhard Weiß, Marco Wolf