By Topic

Neural-network learning and Mark Twain's cat

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
J. A. Anderson ; Dept. of Cognitive & Linguistic Sci., Brown Univ., Providence, RI, USA

In current practice in the engineering community, neural networks are used as only one useful class of adaptive pattern recognizer. Neural networks, however, are far more than devices that can learn accurate input-output transformation or form good category boundaries for pattern classifiers. They are a new form of computer, good at some unfamiliar problems, but quite poor at some familiar ones. An application involving a neural network learning some elementary arithmetic is discussed. It is shown that a simple network program can be implemented by differential weighting of the input data vector. In favorable cases the programming vector can be estimated by seeing relatively few examples of the output, if the task and the structure of the data allow it. Therefore, easy programming is allowed in only a limited domain, controlled by the data representation.<>

Published in:

IEEE Communications Magazine  (Volume:30 ,  Issue: 9 )