English, Article edition: A MATHEMATICAL THEORY FOR LEARNING, AND ITS APPLICATION TO TIME-VARYING COMPUTED TOMOGRAPHY LEONID PERLOVSKY; ROSS DEMING

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Bookmark: http://trove.nla.gov.au/version/132874
Physical Description
  • article
Language
  • English

Edition details

Title
  • A MATHEMATICAL THEORY FOR LEARNING, AND ITS APPLICATION TO TIME-VARYING COMPUTED TOMOGRAPHY
Author
  • LEONID PERLOVSKY
  • ROSS DEMING
Physical Description
  • article
Notes
  • The brain has evolved to enable organisms to survive in a complicated and dynamic world. Its operation is based upon a priori models of the environment which are adapted, during learning, in response to new and changing stimuli. The same qualities that make biological learning mechanisms ideal for organisms make their underlying mathematical algorithms ideal for certain technological applications, especially those concerned with understanding the physical processes giving rise to complicated data sets. In this paper, we offer a mathematical model for the underlying mechanisms of biological learning, and we show how this mathematical approach to learning can yield a solution to the problem of imaging time-varying objects from X-ray computed tomographic (CT) data. This problem relates to several practical aspects of CT imaging including the correction of motion artifacts caused by patient movement or breathing.
  • Mind, learning, intelligence, mixture model, computed tomography, expectation maximization, relative entropy, log-likelihood
  • RePEc:wsi:nmncxx:v:01:y:2005:i:01:p:147-171
Language
  • English
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