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ICRA
2007
IEEE
125views Robotics» more  ICRA 2007»
16 years 29 days ago
Single-Query Motion Planning with Utility-Guided Random Trees
— Randomly expanding trees are very effective in exploring high-dimensional spaces. Consequently, they are a powerful algorithmic approach to sampling-based single-query motion p...
Brendan Burns, Oliver Brock
COLT
2008
Springer
15 years 8 months ago
Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic Domains
We propose a model-based learning algorithm, the Adaptive Aggregation Algorithm (AAA), that aims to solve the online, continuous state space reinforcement learning problem in a de...
Andrey Bernstein, Nahum Shimkin
NIPS
2000
15 years 8 months ago
The Kernel Trick for Distances
A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearl...
Bernhard Schölkopf
AAAI
2012
13 years 9 months ago
Towards Discovering What Patterns Trigger What Labels
In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input spa...
Yu-Feng Li, Ju-Hua Hu, Yuang Jiang, Zhi-Hua Zhou
GECCO
2009
Springer
135views Optimization» more  GECCO 2009»
15 years 11 months ago
Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed
The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In...
Anne Auger