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Parallelism Increases Iterative Learning Power

  • John Case
  • Samuel E. MoeliusIII
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4754)

Abstract

Iterative learning (\(\textbf{It}\)-learning) is a Gold-style learning model in which each of a learner’s output conjectures may depend only upon the learner’s current conjecture and the current input element. Two extensions of the \(\textbf{It}\)-learning model are considered, each of which involves parallelism. The first is to run, in parallel, distinct instantiations of a single learner on each input element. The second is to run, in parallel, n individual learners incorporating the first extension, and to allow the n learners to communicate their results. In most contexts, parallelism is only a means of improving efficiency. However, as shown herein, learners incorporating the first extension are more powerful than \(\textbf{It}\)-learners, and, collective learners resulting from the second extension increase in learning power as n increases. Attention is paid to how one would actually implement a learner incorporating each extension. Parallelism is the underlying mechanism employed.

Keywords

Input Sequence Initial Segment Individual Learner Computable Function Total Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • John Case
    • 1
  • Samuel E. MoeliusIII
    • 1
  1. 1.Department of Computer & Information Sciences, University of Delaware, 103 Smith Hall, Newark, DE 19716USA

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