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GEM
2008
15 years 8 months ago
Evaluating a Parallel Evolutionary Algorithm on the Chess Endgame Problem
Classifying the endgame positions in Chess can be challenging for humans and is known to be a difficult task in machine learning. An evolutionary algorithm would seem to be the ide...
Wayne Iba, Kelsey Marshman, Benjamin Fisk
ML
2002
ACM
146views Machine Learning» more  ML 2002»
15 years 6 months ago
Kernel Matching Pursuit
Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
Pascal Vincent, Yoshua Bengio
EUROCOLT
1995
Springer
15 years 10 months ago
A decision-theoretic generalization of on-line learning and an application to boosting
k. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multi...
Yoav Freund, Robert E. Schapire
ML
2012
ACM
413views Machine Learning» more  ML 2012»
14 years 2 months ago
Gradient-based boosting for statistical relational learning: The relational dependency network case
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are...
Sriraam Natarajan, Tushar Khot, Kristian Kersting,...
FDG
2009
ACM
15 years 4 months ago
Toward a domain-independent framework to automate scaffolding of task-based learning in digital games
This poster describes a framework that automatically generates learning support scaffolds to guide task-based learning. The aim is to combine the exploratory learning principles p...
James M. Thomas, R. Michael Young