Connectionist Models for Intelligent Computation.
Abstract
We have continued our study of higher order neural networks. The superior processing power capacity and speed of the higher order neural network has been demonstrated for many tasks including text to speech, character recognitions, noise removal, time series prediction etc. Currently, we are applying it to the speech recognition problem. We have constructed a neural network to learn the task of stereopsis from random dot stereogram. The connection weights of the network are computed analytically from the Hebbion learning rule. The results show that the continuity and uniqueness constraints first proposed by Marr and Poggio are learned automatically. We proposed a novel scheme (PSIN) to automatically build a neural network while learning. The new scheme takes advantage of both the parallel and sequential strategies to solve a pattern classification or decision problem. We optimize an entropy measure to encourage the network to extract the best feature first to classify the pattern. Preliminary test of this new scheme shows that PSIN performs superior than the back propagation scheme in hard problems. (KR)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 31, 1988
- Accession Number
- ADA200445
Entities
People
- H. H. Chen
- Y. C. Lee
Organizations
- University of Maryland