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CVPR
2007
IEEE
16 years 1 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»
14 years 11 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»
15 years 6 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
15 years 1 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»
14 years 11 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