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» Learning Taxonomies by Dependence Maximization
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NECO
2007
150views more  NECO 2007»
14 years 12 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
AI
2006
Springer
15 years 12 days ago
Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering
This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. ...
Ole J. Mengshoel, David C. Wilkins, Dan Roth
CVPR
2006
IEEE
16 years 2 months ago
Using Dependent Regions for Object Categorization in a Generative Framework
"Bag of words" models have enjoyed much attention and achieved good performances in recent studies of object categorization. In most of these works, local patches are mo...
Gang Wang, Ye Zhang, Fei-Fei Li 0002
ML
2000
ACM
15 years 4 days ago
Maximizing Theory Accuracy Through Selective Reinterpretation
Existing methods for exploiting awed domain theories depend on the use of a su ciently large set of training examples for diagnosing and repairing aws in the theory. In this paper,...
Shlomo Argamon-Engelson, Moshe Koppel, Hillel Walt...
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JCAL
2000
90views more  JCAL 2000»
15 years 4 days ago
Gender preferences for multimedia interfaces
This study examined the gender differences in the preferences to varying designs of multimedia learning interfaces. In the study it was assumed that design characteristics add to t...
David Passig, Haya Levin