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» Neural Network Algorithms for the p-Median Problem
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ISNN
2004
Springer
15 years 4 months ago
Sparse Bayesian Learning Based on an Efficient Subset Selection
Based on rank-1 update, Sparse Bayesian Learning Algorithm (SBLA) is proposed. SBLA has the advantages of low complexity and high sparseness, being very suitable for large scale pr...
Liefeng Bo, Ling Wang, Licheng Jiao
ICANN
2009
Springer
15 years 3 months ago
Constrained Learning Vector Quantization or Relaxed k-Separability
Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from ...
Marek Grochowski, Wlodzislaw Duch
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
15 years 5 months ago
Generating large-scale neural networks through discovering geometric regularities
Connectivity patterns in biological brains exhibit many repeating motifs. This repetition mirrors inherent geometric regularities in the physical world. For example, stimuli that ...
Jason Gauci, Kenneth O. Stanley
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
15 years 4 months ago
Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of sma
Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient i...
Emily Rose Holzinger, Carrie C. Buchanan, Scott M....
CEC
2009
IEEE
15 years 6 months ago
Evolving modular neural-networks through exaptation
— Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living...
Jean-Baptiste Mouret, Stéphane Doncieux