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» On learning with dissimilarity functions
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ICML
2007
IEEE
15 years 10 months ago
Intractability and clustering with constraints
Clustering with constraints is a developing area of machine learning. Various papers have used constraints to enforce particular clusterings, seed clustering algorithms and even l...
Ian Davidson, S. S. Ravi
IROS
2009
IEEE
155views Robotics» more  IROS 2009»
15 years 4 months ago
Active learning using mean shift optimization for robot grasping
— When children learn to grasp a new object, they often know several possible grasping points from observing a parent’s demonstration and subsequently learn better grasps by tr...
Oliver Kroemer, Renaud Detry, Justus H. Piater, Ja...
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 2 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
EUROCOLT
1999
Springer
15 years 2 months ago
Learning Range Restricted Horn Expressions
We study the learnability of first order Horn expressions from equivalence and membership queries. We show that the class of expressions where every term in the consequent of a c...
Roni Khardon
GECCO
2006
Springer
156views Optimization» more  GECCO 2006»
15 years 1 months ago
Improving GP classifier generalization using a cluster separation metric
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Ashley George, Malcolm I. Heywood