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AAAI
2010
15 years 1 months ago
G-Optimal Design with Laplacian Regularization
In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attem...
Chun Chen, Zhengguang Chen, Jiajun Bu, Can Wang, L...
TSMC
2008
117views more  TSMC 2008»
14 years 10 months ago
Discovery of High-Level Behavior From Observation of Human Performance in a Strategic Game
This paper explores the issues faced in creating a sys-4 tem that can learn tactical human behavior merely by observing5 a human perform the behavior in a simulation. More specific...
Brian S. Stensrud, Avelino J. Gonzalez
NAACL
2003
15 years 1 months ago
Automatic Extraction of Semantic Networks from Text using Leximancer
Leximancer is a software system for performing conceptual analysis of text data in a largely language independent manner. The system is modelled on Content Analysis and provides u...
Andrew E. Smith
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
15 years 5 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
IJCNN
2008
IEEE
15 years 6 months ago
Active Meta-Learning with Uncertainty Sampling and Outlier Detection
Abstract— Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this cont...
Ricardo Bastos Cavalcante Prudêncio, Teresa ...