reynolds@park.bu.edu (John Reynolds) (05/23/91)
Hi, Surya.
Recently, Stephen Grossberg, Gail Carpenter and I have introduced a
supervised Adaptive Resonance Theory (ART) network, called ARTMAP,
which can use knowledge about class membership to cluster patterns
meaningfully.
On a trial by trial basis it develops a highly efficient tessellation
of the input space which maps each input cluster onto an appropriate
output cluster.
In the case of known class membership, each distinct output vector
could merely be a label of class membership. In the more general
case, the output vectors reflect central tendencies or critical
feature patterns of output vector clusters.
THE BASIC ALGORITHM:
Each time an input pattern is assigned to a cluster corresponding to
an output cluster that does not include the output vector, the input
tessellation is changed by the minimum amount necessary to correctly
classify the input vector.
Specifically, the system automatically identifies clusters which
(1) are associated with the output cluster containing the output
pattern and (2) will, after learning, code the current input.
If such clusters exist, it identifies the one that will undergo
the minimum change in order to claim the current input pattern.
If there are no such clusters, it creates a new cluster which codes
the current input and associates it with the correct output cluster.
In this way it can classify arbitrarily many, arbitrarily ordered
vectors into recognition categories based on predictive success. It
conjointly maximizes predictive generalization and minimizes
predictive error by linking predictive success to category size on a
trial-by-trial basis, using only local operations.
The system was presented a couple of weeks ago at the Wang Institute
Conference on Neural Networks for Vision and Image Processing, and it
will also appear at the upcoming IJCNN meeting (Lecture, Friday, July
12, Session 2, 9:10 - 9:30AM). It will be discussed in an upcoming
issue of Neural Networks (Neural Networks, 4, in press), and it is now
available as Technical Report CAS/CNS-TR-91-001.
If you would like a copy of the technical report, please write to the
following address:
Boston University Center for Adaptive Systems
and Cognitive and Neural Systems Department
111 Cummington Street, Rm. 244
Boston, MA 02215
or contact Cindy Suchta (cindy@park.bu.edu) to request a copy of the
technical report. Be sure to include the technical report number.
-John Reynolds
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