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» Metacognitive Control and Optimal Learning
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ML
2008
ACM
14 years 9 months ago
Margin-based first-order rule learning
Abstract We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the comp...
Ulrich Rückert, Stefan Kramer
COLT
2003
Springer
15 years 2 months ago
Learning with Rigorous Support Vector Machines
We examine the so-called rigorous support vector machine (RSVM) approach proposed by Vapnik (1998). The formulation of RSVM is derived by explicitly implementing the structural ris...
Jinbo Bi, Vladimir Vapnik
CHI
2010
ACM
15 years 4 months ago
Interactive optimization for steering machine classification
Interest has been growing within HCI on the use of machine learning and reasoning in applications to classify such hidden states as user intentions, based on observations. HCI res...
Ashish Kapoor, Bongshin Lee, Desney S. Tan, Eric H...
GECCO
2008
Springer
182views Optimization» more  GECCO 2008»
14 years 10 months ago
Scaling ant colony optimization with hierarchical reinforcement learning partitioning
This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. Th...
Erik J. Dries, Gilbert L. Peterson
GECCO
2004
Springer
115views Optimization» more  GECCO 2004»
15 years 3 months ago
Robotic Control Using Hierarchical Genetic Programming
In this paper, we compare the performance of hierarchical GP methods (Automatically Defined Functions, Module Acquisition, Adaptive Representation through Learning) with the canon...
Marcin L. Pilat, Franz Oppacher