Abstract
High-order neural networks have been shown to have impressive computational, storage, and learning capabilities. This performance is because the order or structure of a high-order neural network can be tailored to the order or structure of a problem. Thus, a neural network designed for a particular class of problems becomes specialized but also very efficient in solving those problems. Furthermore, a priori knowledge, such as geometric invariances, can be encoded in high-order networks. Because this knowledge does not have to be learned, these networks are very efficient in solving problems that utilize this knowledge.
Original language | English (US) |
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Pages (from-to) | 4972-4978 |
Number of pages | 7 |
Journal | Applied optics |
Volume | 26 |
Issue number | 23 |
DOIs | |
State | Published - Dec 1987 |
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Engineering (miscellaneous)
- Electrical and Electronic Engineering