Learning evaluation functions for global optimization and Boolean satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fast probabilistic modeling for combinatorial optimization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Model selection based on minimum description length
Journal of Mathematical Psychology
On the complexity of additive clustering models
Journal of Mathematical Psychology
Determining the dimensionality of multidimensional scaling representations for cognitive modeling
Journal of Mathematical Psychology
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
An empirical evaluation of Chernoff faces, star glyphs, and spatial visualizations for binary data
APVis '03 Proceedings of the Asia-Pacific symposium on Information visualisation - Volume 24
Visualizations of binary data: a comparative evaluation
International Journal of Human-Computer Studies
Computational Statistics & Data Analysis
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Additive clustering was originally developed within cognitive psychology to enable the development of featural models of human mental representation. The representational flexibility of additive clustering, however, suggests its more general application to modeling complicated relationships between objects in non-psychological domains of interest. This paper describes, demonstrates, and evaluates a simple method for learning additive clustering models, based on the combinatorial optimization approach known as Population-Based Incremental Learning. The performance of this new method is shown to be comparable with previously developed methods over a set of ‘benchmark’ data sets. In addition, the method developed here has the potential, by using a Bayesian analysis of model complexity that relies on an estimate of data precision, to determine the appropriate number of clusters to include in a model.