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» Apprenticeship learning using linear programming
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CORR
2006
Springer
130views Education» more  CORR 2006»
14 years 9 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...
ML
1998
ACM
117views Machine Learning» more  ML 1998»
14 years 9 months ago
Learning Team Strategies: Soccer Case Studies
We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy, but may behave di erently due to position-dependent inputs. All...
Rafal Salustowicz, Marco Wiering, Jürgen Schm...
ACL
2010
14 years 7 months ago
Global Learning of Focused Entailment Graphs
We propose a global algorithm for learning entailment relations between predicates. We define a graph structure over predicates that represents entailment relations as directed ed...
Jonathan Berant, Ido Dagan, Jacob Goldberger
GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
15 years 1 months ago
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively coevolves a population of adaptive mappings and associated genotypes is...
Garnett Carl Wilson, Malcolm I. Heywood
80
Voted
ICML
2008
IEEE
15 years 10 months ago
Learning to classify with missing and corrupted features
After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularl...
Ofer Dekel, Ohad Shamir