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» On learning algorithm selection for classification
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PKDD
2010
Springer
179views Data Mining» more  PKDD 2010»
14 years 10 months ago
Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-Like Exploration
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Tobias Jung, Peter Stone
145
Voted
CVPR
2006
IEEE
16 years 2 months ago
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Kristen Grauman, Trevor Darrell
GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
15 years 4 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
103
Voted
IJCAI
2007
15 years 2 months ago
Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road Autonomous Driving
The mobile robotics community has traditionally addressed motion planning and navigation in terms of steering decisions. However, selecting the best speed is also important – be...
David Stavens, Gabriel Hoffmann, Sebastian Thrun
CORR
2012
Springer
183views Education» more  CORR 2012»
13 years 8 months ago
Learning Determinantal Point Processes
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
Alex Kulesza, Ben Taskar