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Analysis of the Convergence and Generalization of AA1

Christophe Giraud-Carrier, Tony Martinez, Analysis of the Convergence and Generalization of AA1. Journal of Parallel and Distributed Computing, 26 (1). ISSN 0743-7315, pp. 125–131. April 1995. PDF, 50 Kbytes.


AA1 is an incremental learning algorithm for Adaptive Sefl-Organizing Concurrent Systems (ASOCS). ASOCS are self-organizing, dynamically growing networks of computing nodes. AA1 learns by discrimination and implements knowledge in a distributed fashion over all the nodes. This paper reviews AA1 from the perspective of convergence and generalization. A formal proof that AA1 converges on any arbitrary Boolean instance set is given. A discussion of generalization and other aspects of AA1, including the problem of handling inconsistency, follows. Results of simulations with real-world data are presented. They show that AA1 gives promising generalization.

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