Classification of Handwritten Digits using a RAM Neural Net Architecture-Reference-Cited by-同舟云学术

Classification of Handwritten Digits using a RAM Neural Net Architecture

Published:1997-02 Issue:01 Volume:08 Page:17-25
ISSN:0129-0657
Container-title:International Journal of Neural Systems
language:en
Short-container-title:Int. J. Neur. Syst.

Author:

Jørgensen Thomas Martini¹

Affiliation:

1. Optics & Fluid Dynamics Department, Risø National Laboratory, P.O. Box 49, DK-4000 Roskilde, Denmark

Abstract

Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The result are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces negative weights into the RAM net is reported. The results obtained on the task of recognizing handwritten digits is comparable with the best performances reported in the literature.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0129065797000045

Reference4 articles.

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Logic-Based Neural Network for Pattern Correction;2022 International Conference on Smart Systems and Power Management (IC2SPM);2022-11-10

2. Weightless Neural Models: An Overview;Women in Computational Intelligence;2022

3. A massively parallel pipelined reconfigurable design for M-PLN based neural networks for efficient image classification;Neurocomputing;2016-03

4. A novel neuro-fuzzy classifier based on weightless neural network;Proceedings of the International Conference and Workshop on Emerging Trends in Technology;2010-02-26

5. On a hybrid weightless neural system;International Journal of Bio-Inspired Computation;2009