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CVPR
2007
IEEE
14 years 9 months ago
Optimizing Distribution-based Matching by Random Subsampling
We boost the efficiency and robustness of distributionbased matching by random subsampling which results in the minimum number of samples required to achieve a specified probabili...
Alex Po Leung, Shaogang Gong
TSMC
2008
99views more  TSMC 2008»
13 years 7 months ago
Robust Regularized Kernel Regression
Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual fo...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
GECCO
2007
Springer
164views Optimization» more  GECCO 2007»
14 years 1 months ago
A study of mutational robustness as the product of evolutionary computation
This paper investigates the ability of a tournament selection based genetic algorithm to find mutationally robust solutions to a simple combinatorial optimization problem. Two di...
Justin Schonfeld
AAAI
1998
13 years 9 months ago
Optimal 2D Model Matching Using a Messy Genetic Algorithm
A Messy Genetic Algorithm is customized toflnd'optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which...
J. Ross Beveridge
TCAD
2008
136views more  TCAD 2008»
13 years 7 months ago
A Geometric Programming-Based Worst Case Gate Sizing Method Incorporating Spatial Correlation
We present an efficient optimization scheme for gate sizing in the presence of process variations. Our method is a worst-case design scheme, but it reduces the pessimism involved i...
Jaskirat Singh, Zhi-Quan Luo, Sachin S. Sapatnekar