Accession Number : ADA261182


Title :   Computation and Learning in Neural Networks With Binary Weights


Descriptive Note : Final rept. 1 Sep 1989-31 Aug 1992,


Corporate Author : MOORE SCHOOL OF ELECTRICAL ENGINEERING PHILADELPHIA PA


Personal Author(s) : Venkatesh, Santosh S


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a261182.pdf


Report Date : 28 Nov 1992


Pagination or Media Count : 325


Abstract : Under the aegis of the AFOSR grant they have been investigating computational learning attributes of networks of formal neurons. The formal neurons considered are linear threshold elements which produce binary outputs based on the sign of a linear form of a set of inputs. The researchers have been interested in (1) exploring the theoretical limitations on what can be computed or learnt in neural network architectures, and (2) developing and analyzing learning algorithms which specify weights as a function of a set of examples of a computation.


Descriptors :   *NEURAL NETS , *COMPUTATIONS , *LEARNING , ALGORITHMS , COMPUTER ARCHITECTURE , WEIGHT , COMPUTER AIDED INSTRUCTION , NERVE CELLS , LIMITATIONS , INPUT , OUTPUT


Subject Categories : Psychology
      Computer Systems


Distribution Statement : APPROVED FOR PUBLIC RELEASE