A MATHEMATICAL THEORY FOR LEARNING, AND ITS APPLICATION TO TIME-VARYING COMPUTED TOMOGRAPHY

Leonid Perlovsky () and Ross Deming ()
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Leonid Perlovsky: Sensors Directorate, Air Force Research Laboratory, 80 Scott Drive, Hanscom AFB, MA 01731, USA
Ross Deming: Anteon Corporation, Air Force Research Laboratory, Sensors Directorate, 80 Scott Drive, Hanscom AFB, MA 01731, USA

New Mathematics and Natural Computation (NMNC), 2005, vol. 01, issue 01, 147-171

Abstract: The brain has evolved to enable organisms to survive in a complicated and dynamic world. Its operation is based upona priorimodels 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.

Keywords: Mind; learning; intelligence; mixture model; computed tomography; expectation maximization; relative entropy; log-likelihood (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1142/S1793005705000081

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