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» Adaptive Learning in Machine Summarization
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119
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ICML
2005
IEEE
16 years 3 months ago
Supervised versus multiple instance learning: an empirical comparison
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
Soumya Ray, Mark Craven
119
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ESANN
2008
15 years 4 months ago
Generalized matrix learning vector quantizer for the analysis of spectral data
The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in t...
Petra Schneider, Frank-Michael Schleif, Thomas Vil...
ICALT
2009
IEEE
15 years 9 months ago
Authoring for E-learning 2.0: A Case Study
E-learning 2.0 is a term refers to the second generation of e-learning, which uses the technologies of the Social Web, such as collaborative authoring and social annotation, in or...
Fawaz Ghali, Alexandra I. Cristea
191
Voted
IWCLS
2007
Springer
15 years 8 months ago
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most c...
Larry Bull
ML
2007
ACM
104views Machine Learning» more  ML 2007»
15 years 2 months ago
A general criterion and an algorithmic framework for learning in multi-agent systems
We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one’s rewards in the presence of other agents who might also...
Rob Powers, Yoav Shoham, Thuc Vu