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NECO
2007
150views more  NECO 2007»
14 years 9 months ago
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
Dorit Baras, Ron Meir
KDD
2008
ACM
181views Data Mining» more  KDD 2008»
15 years 10 months ago
Learning subspace kernels for classification
Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kern...
Jianhui Chen, Shuiwang Ji, Betul Ceran, Qi Li, Min...
CORR
2002
Springer
142views Education» more  CORR 2002»
14 years 9 months ago
Learning Algorithms for Keyphrase Extraction
Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases ...
Peter D. Turney
WSDM
2012
ACM
259views Data Mining» more  WSDM 2012»
13 years 5 months ago
Learning recommender systems with adaptive regularization
Many factorization models like matrix or tensor factorization have been proposed for the important application of recommender systems. The success of such factorization models dep...
Steffen Rendle
TKDE
2010
182views more  TKDE 2010»
14 years 8 months ago
MILD: Multiple-Instance Learning via Disambiguation
In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set ...
Wu-Jun Li, Dit-Yan Yeung