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» Learning Relations Using Collocations
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ICML
2009
IEEE
14 years 8 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
ICML
2005
IEEE
14 years 8 months ago
Predicting relative performance of classifiers from samples
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
Rui Leite, Pavel Brazdil
ICML
2007
IEEE
14 years 8 months ago
Learning a meta-level prior for feature relevance from multiple related tasks
In many prediction tasks, selecting relevant features is essential for achieving good generalization performance. Most feature selection algorithms consider all features to be a p...
Su-In Lee, Vassil Chatalbashev, David Vickrey, Dap...
IJCAI
1993
13 years 8 months ago
HYDRA: A Noise-tolerant Relational Concept Learning Algorithm
Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the ne...
Kamal M. Ali, Michael J. Pazzani
ICPR
2010
IEEE
13 years 5 months ago
Semi-supervised Graph Learning: Near Strangers or Distant Relatives
In this paper, an easily implemented semi-supervised graph learning method is presented for dimensionality reduction and clustering, using the most of prior knowledge from limited...
Weifu Chen, Guocan Feng