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Learning and Geometry: Computational Approaches (Paperback, Softcover reprint of the original 1st ed. 1996)
Loot Price: R2,787
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Learning and Geometry: Computational Approaches (Paperback, Softcover reprint of the original 1st ed. 1996)
Series: Progress in Computer Science and Applied Logic, 14
Expected to ship within 10 - 15 working days
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The field of computational learning theory arose out of the desire
to for mally understand the process of learning. As potential
applications to artificial intelligence became apparent, the new
field grew rapidly. The learning of geo metric objects became a
natural area of study. The possibility of using learning techniques
to compensate for unsolvability provided an attraction for individ
uals with an immediate need to solve such difficult problems.
Researchers at the Center for Night Vision were interested in
solving the problem of interpreting data produced by a variety of
sensors. Current vision techniques, which have a strong geometric
component, can be used to extract features. However, these
techniques fall short of useful recognition of the sensed objects.
One potential solution is to incorporate learning techniques into
the geometric manipulation of sensor data. As a first step toward
realizing such a solution, the Systems Research Center at the
University of Maryland, in conjunction with the Center for Night
Vision, hosted a Workshop on Learning and Geometry in January of
1991. Scholars in both fields came together to learn about each
others' field and to look for common ground, with the ultimate goal
of providing a new model of learning from geometrical examples that
would be useful in computer vision. The papers in the volume are a
partial record of that meeting."
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