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» Evaluating learning algorithms and classifiers
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AAAI
2008
15 years 7 months ago
Semi-supervised Classification Using Local and Global Regularization
In this paper, we propose a semi-supervised learning (SSL) algorithm based on local and global regularization. In the local regularization part, our algorithm constructs a regular...
Fei Wang, Tao Li, Gang Wang, Changshui Zhang
136
Voted
ATAL
2004
Springer
15 years 10 months ago
Best-Response Multiagent Learning in Non-Stationary Environments
This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learn...
Michael Weinberg, Jeffrey S. Rosenschein
153
Voted
IUI
2004
ACM
15 years 10 months ago
Evaluating adaptive user profiles for news classification
Never before have so many information sources been available. Most are accessible on-line and some exist on the Internet alone. However, this large information quantity makes inte...
Ricardo Carreira, Jaime M. Crato
IUI
2009
ACM
16 years 1 months ago
Learning to generalize for complex selection tasks
Selection tasks are common in modern computer interfaces: we are often required to select a set of files, emails, data entries, and the like. File and data browsers have sorting a...
Alan Ritter, Sumit Basu
GECCO
2003
Springer
114views Optimization» more  GECCO 2003»
15 years 9 months ago
Learning the Ideal Evaluation Function
Abstract. Designing an adequate fitness function requiressubstantial knowledge of a problem and of features that indicate progress towards a solution. Coevolution takes the human ...
Edwin D. de Jong, Jordan B. Pollack