Behind, Between, and Beyond Anthropomorphic Models

The plurality of models of ultimate reality is a central problem for religious philosophy. This essay sketches what is involved in mounting comparative inquiries across the plurality of models. In order to illustrate what advance would look like in such a comparative inquiry, an argument is presented to show that highly anthropomorphic models of ultimate reality are inferior to a number of competitors. This paper was delivered as a keynote address during the APA Pacific 2007 Mini-Conference on Models of God.

Fuzzy ARTMAP, Slow Learning and Probability Estimation

A nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is here described. Because the procedure does not make a priori assumptions about underlying probability distributions, it yields accurate estimates on a wide variety of prediction tasks. Fuzzy ARTMAP is used to perform probability estimation in two different modes. In a 'slow-learning' mode, input-output associations change slowly, with the strength of each association computing a conditional probability estimate. In 'max-nodes' mode, a fixed number of categories are coded during an initial fast learning interval, and weights are then tuned by slow learning. Simulations illustrate system performance on tasks in which various numbers of clusters in the set of input vectors mapped to a given class.