The Rex Leopold II Model: Application of the Reduced Set Density Estimator to Human Categorization.

Reduction techniques are important tools in machine learning and pattern recognition. In this article, we demonstrate how a kernel-based density estimator can be used as a tool for understanding human category representation. Despite the dominance of exemplar models of categorization, there is still ambiguity about the number of exemplars stored in memory. Here, we illustrate that by omitting exemplars categorization performance is not affected.