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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Coward, A. Kumar, S. Hung, A. Jullien, G.A. |
| Copyright Year | 1993 |
| Description | Author affiliation: Northern TelCom Ltd., Ottawa, Ont., Canada (Coward, A.) |
| Abstract | The authors present a new architecture for machine perception of objects using a hierarchical pattern extraction technique. The resulting architecture is a neural network with ordinary logic gates as the neurons and simple heuristic pattern association techniques as the training algorithm. The architecture consists of a multilayer network of neurons and a final layer with a single neuron. The interconnections between the different layers are determined on the fly during the training process. Most of the data that is to be processed during training can be represented as binary values; likewise, all synapse values are binary. The application area is in the recognition of a single object type from a field of object types, a common problem in machine perception. The authors introduce the architecture and training algorithm, and present initial results using statistically defined objects. |
| Starting Page | 19 |
| Ending Page | 26 |
| File Size | 650483 |
| Page Count | 8 |
| File Format | |
| ISBN | 0818654201 |
| DOI | 10.1109/CAMP.1993.622453 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1993-12-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neurons Logic gates Telecommunications Neural networks Multi-layer neural network Object recognition Pattern classification Feedforward systems Nonhomogeneous media Numerical stability |
| Content Type | Text |
| Resource Type | Article |
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