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» Model Selection in an Information Economy: Choosing What to ...
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CI
2002
92views more  CI 2002»
13 years 4 months ago
Model Selection in an Information Economy: Choosing What to Learn
As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance...
Christopher H. Brooks, Robert S. Gazzale, Rajarshi...
EMNLP
2010
13 years 2 months ago
What a Parser Can Learn from a Semantic Role Labeler and Vice Versa
In many NLP systems, there is a unidirectional flow of information in which a parser supplies input to a semantic role labeler. In this paper, we build a system that allows inform...
Stephen A. Boxwell, Dennis Mehay, Chris Brew
CVPR
2009
IEEE
14 years 12 months ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
COLT
2008
Springer
13 years 6 months ago
Model Selection and Stability in k-means Clustering
Clustering Stability methods are a family of widely used model selection techniques applied in data clustering. Their unifying theme is that an appropriate model should result in ...
Ohad Shamir, Naftali Tishby
IEICET
2007
114views more  IEICET 2007»
13 years 4 months ago
Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion
In order to obtain better learning results in supervised learning, it is important to choose model parameters appropriately. Model selection is usually carried out by preparing a ...
Shun Gokita, Masashi Sugiyama, Keisuke Sakurai