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» A Guided Monte Carlo Approach to Optimization Problems
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ICML
2007
IEEE
15 years 10 months ago
Most likely heteroscedastic Gaussian process regression
This paper presents a novel Gaussian process (GP) approach to regression with inputdependent noise rates. We follow Goldberg et al.'s approach and model the noise variance us...
Kristian Kersting, Christian Plagemann, Patrick Pf...
AAAI
1994
14 years 11 months ago
Case-Based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains
1 We have developed an approach to acquire complicated user optimization criteria and use them to guide iterative solution improvement. The eectiveness of the approach was tested ...
Katia P. Sycara, Kazuo Miyashita
CVPR
2008
IEEE
16 years 6 hour ago
Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking
Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During...
Zhaozheng Yin, Robert T. Collins
CEC
2009
IEEE
15 years 4 months ago
Parameter control in Differential Evolution for constrained optimization
In this Chapter we present the modification of a Differential Evolution algorithm to solve constrained optimization problems. The changes include a deterministic and a self-adapti...
Efrén Mezura-Montes, A. G. Palomeque-Ortiz
ESWA
2007
100views more  ESWA 2007»
14 years 9 months ago
Using memetic algorithms with guided local search to solve assembly sequence planning
The goal of assembly planning consists in generating feasible sequences to assemble a product and selecting an efficient assembly sequence from which related constraint factors su...
Hwai-En Tseng, Wen-Pai Wang, Hsun-Yi Shih