44 search results - page 6 / 9 » On numerical optimization theory of infinite kernel learning |
188
click to vote
COMPGEOM
2011
ACM
14 years 2 months ago
2011
ACM
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
GECCO
2005
Springer
15 years 4 months ago
2005
Springer
This paper presents a kernel density estimation method by means of real-coded crossovers. Estimation of density algorithms (EDAs) are evolutionary optimization techniques, which d...
COLT
2010
Springer
14 years 9 months ago
2010
Springer
We consider the problem of planning in a stochastic and discounted environment with a limited numerical budget. More precisely, we investigate strategies exploring the set of poss...
NIPS
2004
15 years 16 days ago
2004
A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf distance. The Laplacian pdf distance exhibits a remarkabl...
109
click to vote
ICRA
2010
IEEE
14 years 9 months ago
2010
IEEE
— Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far d...