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JCP
2007
143views more  JCP 2007»
13 years 5 months ago
Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization
Abstract— We describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-bestpaths algorithm. We consider an a...
Nicolas Chapados, Yoshua Bengio
ICMCS
2006
IEEE
130views Multimedia» more  ICMCS 2006»
13 years 11 months ago
Directional Weighting-Based Demosaicking Algorithm for Noisy CFA Environments
Captured CFA data by image sensors like CCD/or CMOS are often corrupted by noises. To produce high quality images acquired by CCD/CMOS digital cameras, the problem of noise needs ...
Hung-Yi Lo, Tsung-Nan Lin, Chih-Lung Hsu, Cheng-hs...
GECCO
2010
Springer
183views Optimization» more  GECCO 2010»
13 years 9 months ago
Benchmarking a MOS-based algorithm on the BBOB-2010 noisy function testbed
In this paper, a hybrid algorithm based on the Multiple Offspring Sampling framework is presented and benchmarked on the BBOB-2010 noisy testbed. MOS allows the seamless combinat...
Antonio LaTorre, Santiago Muelas, José Mar&...
EC
2008
103views ECommerce» more  EC 2008»
13 years 5 months ago
A Graphical Model for Evolutionary Optimization
We present a statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata. Because No Free Lunch theorems di...
Christopher K. Monson, Kevin D. Seppi
GECCO
2010
Springer
182views Optimization» more  GECCO 2010»
13 years 9 months ago
Benchmarking CMA-EGS on the BBOB 2010 noisy function testbed
This paper describes the implementation and the results for CMA-EGS on the BBOB 2010 noisy testbed. The CMAEGS is a hybrid strategy which combines elements from gradient search an...
Steffen Finck, Hans-Georg Beyer