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GECCO
2006
Springer
161views Optimization» more  GECCO 2006»
15 years 11 months ago
The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems
1 Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new type o...
Janusz Wojtusiak, Ryszard S. Michalski
ACMACE
2008
ACM
15 years 9 months ago
Real binoculars with virtual functions for mixed environments
Though often desirable, the integration of real and virtual elements in mixed reality environments can be difficult. We propose a number of techniques to facilitate scene explorat...
Andrei Sherstyuk, Kin Lik Wang, Anton Treskunov, J...
NIPS
2001
15 years 8 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
OSDI
1994
ACM
15 years 8 months ago
A Caching Model of Operating System Kernel Functionality
Operating system research has endeavored to develop micro-kernels that provide modularity, reliability and security improvements over conventional monolithic kernels. However, the...
David R. Cheriton, Kenneth J. Duda
SIAMSC
2008
237views more  SIAMSC 2008»
15 years 7 months ago
A Variational Shape Optimization Approach for Image Segmentation with a Mumford--Shah Functional
We introduce a novel computational method for a Mumford-Shah functional, which decomposes a given image into smooth regions separated by closed curves. Casting this as a shape opti...
Günay Dogan, Pedro Morin, Ricardo H. Nochetto