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» Learning Bounds for Domain Adaptation
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ML
2000
ACM
150views Machine Learning» more  ML 2000»
15 years 8 days ago
Adaptive Retrieval Agents: Internalizing Local Context and Scaling up to the Web
This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents. These InfoSpiders browse networked information ...
Filippo Menczer, Richard K. Belew
109
Voted
HIS
2004
15 years 1 months ago
Adaptive Boosting with Leader based Learners for Classification of Large Handwritten Data
Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for Adaptive Boosting method, consists of repeated use of a weak or a base le...
T. Ravindra Babu, M. Narasimha Murty, Vijay K. Agr...
130
Voted
JMLR
2010
162views more  JMLR 2010»
14 years 7 months ago
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...
TSMC
2008
229views more  TSMC 2008»
15 years 11 days ago
A Comprehensive Survey of Multiagent Reinforcement Learning
Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many task...
Lucian Busoniu, Robert Babuska, Bart De Schutter
97
Voted
ICSE
2008
IEEE-ACM
16 years 1 months ago
Time-bounded adaptation for automotive system software
Software is increasingly deployed in vehicles as demand for new functionality increases and cheaper and more powerful hardware becomes available. Likewise, emerging wireless commu...
Aline Senart, Douglas C. Schmidt, Serena Fritsch, ...