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» Learning recommender systems with adaptive regularization
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KI
2002
Springer
13 years 9 months ago
Advantages, Opportunities and Limits of Empirical Evaluations: Evaluating Adaptive Systems
While empirical evaluations are a common research method in some areas of Artificial Intelligence (AI), others still neglect this approach. This article outlines both the opportun...
Stephan Weibelzahl, Gerhard Weber
CDC
2010
IEEE
106views Control Systems» more  CDC 2010»
13 years 4 months ago
Optimal cross-layer wireless control policies using TD learning
We present an on-line crosslayer control technique to characterize and approximate optimal policies for wireless networks. Our approach combines network utility maximization and ad...
Sean P. Meyn, Wei Chen, Daniel O'Neill
APIN
1998
107views more  APIN 1998»
13 years 9 months ago
Multiple Adaptive Agents for Tactical Driving
Abstract. Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However, there is currently no syst...
Rahul Sukthankar, Shumeet Baluja, John Hancock
CCS
2008
ACM
13 years 11 months ago
User-controllable learning of security and privacy policies
Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have succ...
Patrick Gage Kelley, Paul Hankes Drielsma, Norman ...
ICML
2010
IEEE
13 years 11 months ago
Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains
Link prediction is a key technique in many applications such as recommender systems, where potential links between users and items need to be predicted. A challenge in link predic...
Bin Cao, Nathan Nan Liu, Qiang Yang