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CCS Seminar
Friday - May 4, 2007
12:00 noon
Physics Research Building - Room 595
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Professor Gail Carpenter
Cognitive & Neural Systems - Boston University
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"Information Fusion and Hierarchical Knowledge Discovery"

Image fusion has been defined as "the acquisition, processing and
synergistic combination of information provided by various sensors or by
the same sensor in many measuring contexts." (Simone et al., Information
Fusion, 3, 2002, p. 3) When multiple sources provide inconsistent data,
such methods are called upon to select the accurate information
components. As quoted by the International Society of Information Fusion:
"Evaluating the reliability of different information sources is crucial
when the received data reveal some inconsistencies and we have to choose
among various options.

This talk will address a complementary and novel aspect of the information
fusion problem, seeking to derive consistent knowledge from sources that are
paradoxically both inconsistent and accurate. This is a problem that the
human brain solves well. A young child who hears the family pet variously
called Spot, puppy, dog, Dalmatian, mammal, and animal is not only not
alarmed by these conflicting labels but readily uses them to infer
functional relationships that are never explicitly specified.

An ARTMAP neural network derives hierarchical knowledge structures from
nominally inconsistent training data. The overall pattern of distributed
predictions reveals a hierarchy, which guides the production of
consistently layered knowledge. Even though no inter-class relationships,
links, or
probabilities are provided during training, the system derives
relationship rules, confidence estimates, equivalence classes, and
hierarchical structures.


Cheryl Endicott
Administrative Assistant
Center for Computational Science
3 Cummington Street
Boston, MA 02215
tel: 617-358-1470
fax: 617-358-2487
email:cheryle@bu.edu
http://ccs.bu.edu

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