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Extra info for Ancient Europe, 8000 B.C. to A.D. 1000: An Encyclopedia of the Barbarian World
Geographically dissimilar training and testing areas robustly assess regional generalization. In this article, spatial analysis simulations on the Boston Testbed follow this protocol to illustrate ART systems for self-supervised learning, knowledge discovery, and attentional control. Since each system in Applications 1–3 requires no parameter selection, training uses randomly chosen pixels from three strips, with testing on the fourth strip. Application 1: Learning from Experience with Self-Supervised ART Computational models of supervised pattern recognition typically utilize two learning phases.
Complementary mechanisms create a self-stabilizing perceptual/cognitive front end for intelligently manipulating the more labile spatial/motor processes that enable our changeable bodies to act effectively upon a changing world. Some of the existing large-scale ART systems are briefly reviewed here, using visually based systems for definiteness. bu. edu/ steve. attend, invariantly recognize, and look at a valued target object (Fig. 3; Cao, Grossberg, and Markowitz 2011; Chang, Grossberg, and Cao 2014; Fazl, Grossberg, and Mingolla 2009; Foley, Grossberg, and Mingolla 2012; Grossberg, Srinivasan, and Yazdanbakhsh 2014).
It remains to unify and adapt them to particular machine learning applications. Complementary Computing implies that not all of these subsystems could be based on variants of ART. In particular, accumulating experimental and theoretical evidence shows that perceptual/cognitive and spatial/motor processes use different learning, matching, and predictive laws for their complementary functions (Fig. 2). ART-like processing is ubiquitous in perceptual and cognitive processes, including excitatory matching and match-based learning that enables self-stabilizing memories to form.