Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
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Unsupervised learning algorithms realizing topographic mappings are justified by neurobiology while they are useful for multivariate data analysis. In contrast to supervised learning algorithms unsupervised neural networks have their objective function implicitly defined by the learning rule. When considering topographic mapping as an optimization problem, the presence of explicitly defined objective functions becomes essential. In this paper, we show that measures of neighborhood preservation can be used for optimizing and learning topographic mappings by means of evolution strategies. Numerical experiments reveal these measures also being a possible description of the principles governing the learning process of unsupervised neural networks. We argue that quantifying neighborhood preservation provides a link for connecting evolution strategies and unsupervised neural learning algorithms for building hybrid learning architectures.