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GECCO
2006
Springer
137views Optimization» more  GECCO 2006»
13 years 11 months ago
A method for parameter calibration and relevance estimation in evolutionary algorithms
We present and evaluate a method for estimating the relevance and calibrating the values of parameters of an evolutionary algorithm. The method provides an information theoretic m...
Volker Nannen, A. E. Eiben
CSDA
2006
98views more  CSDA 2006»
13 years 7 months ago
Fast estimation algorithm for likelihood-based analysis of repeated categorical responses
Likelihood-based marginal regression modelling for repeated, or otherwise clustered, categorical responses is computationally demanding. This is because the number of measures nee...
Jukka Jokinen
FOCI
2007
IEEE
14 years 2 months ago
Almost All Learning Machines are Singular
— A learning machine is called singular if its Fisher information matrix is singular. Almost all learning machines used in information processing are singular, for example, layer...
Sumio Watanabe
PR
2006
89views more  PR 2006»
13 years 7 months ago
Gaussian fields for semi-supervised regression and correspondence learning
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
Jakob J. Verbeek, Nikos A. Vlassis
ALGOSENSORS
2008
Springer
13 years 9 months ago
Algorithms for Location Estimation Based on RSSI Sampling
In this paper, we re-examine the RSSI measurement model for location estimation and provide the first detailed formulation of the probability distribution of the position of a sens...
Charalampos Papamanthou, Franco P. Preparata, Robe...