Abstract
Systems capable of recognizing and learning two-dimensional patterns can be used in imaging systems and robotic perception systems. The symbolic and neuromorphic methods for pattern processing problems of this type are complementary in character. We present a hybrid system that utilizes components of symbolic and neuromorphic type; we employ two hybrid components that simultaneously operate up on the same data to produce hypotheses about the data. To resolve the potential conflicts in these hypotheses, we propose a method that learns a combination rule based on a set of examples. We employ the method of empirical risk minimization that does not require knowledge about the error probability distributions of the modules. We are building a prototype system to recognize control panels using a vision system.
Partally funded by Virginia's Center for Innovative Technology under grant #INF-90-015, the Department of Energy through Oak Ridge National Laboratory operated by Martin Marietta Energy Systems, Inc., under the contract #19X-SE043V, and by Old Dominion University Summer Faculty Fellowship for 1991.
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Glover, C.W., Oblow, E.M., Rao, N.S.V. (1991). Hybrid pattern recognition system capable of self-modification. In: Ras, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1991. Lecture Notes in Computer Science, vol 542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54563-8_97
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DOI: https://doi.org/10.1007/3-540-54563-8_97
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