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» On learning with dissimilarity functions
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ICML
2008
IEEE
15 years 11 months ago
Boosting with incomplete information
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we pres...
Feng Jiao, Gholamreza Haffari, Greg Mori, Shaojun ...
EVOW
2009
Springer
15 years 4 months ago
Evolutionary Optimization Guided by Entropy-Based Discretization
The Learnable Evolution Model (LEM) involves alternating periods of optimization and learning, performa extremely well on a range of problems, a specialises in achieveing good resu...
Guleng Sheri, David W. Corne
ICML
1999
IEEE
15 years 11 months ago
Approximation Via Value Unification
: Numerical function approximation over a Boolean domain is a classical problem with wide application to data modeling tasks and various forms of learning. A great many function ap...
Paul E. Utgoff, David J. Stracuzzi
GECCO
2007
Springer
192views Optimization» more  GECCO 2007»
15 years 4 months ago
Estimation of fitness landscape contours in EAs
Evolutionary algorithms applied in real domain should profit from information about the local fitness function curvature. This paper presents an initial study of an evolutionary...
Petr Posík, Vojtech Franc
IJCNN
2006
IEEE
15 years 4 months ago
Automated Model Selection (AMS) on Finite Mixtures: A Theoretical Analysis
— From the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed for finite mixtures with a novel property that its maximization can make model...
Jinwen Ma