DocumentCode
1600036
Title
and the perceptron is better than its reputation after all!
Author
Faessler, Angela
Author_Institution
St. Gallen Sch. of Eng., Switzerland
fYear
1996
Firstpage
139
Lastpage
145
Abstract
A large class of functions (in one or more variables) can be approximated by a, generally, multilayer feed-forward network, in which only the weights of the last layer need to be trained. All others can be selected appropriately dependent upon the desired accuracy with which the training examples are to be approximated, but independently of the examples. Thus only a perceptron remains to be trained
Keywords
feedforward neural nets; learning (artificial intelligence); multilayer perceptrons; multilayer feed-forward network; neural network weight training; perceptron; Feedforward systems; Linear regression; Neural networks; Neurons; Polynomials; Zinc;
fLanguage
English
Publisher
ieee
Conference_Titel
Neuro-Fuzzy Systems, 1996. AT'96., International Symposium on
Conference_Location
Lausanne
Print_ISBN
0-7803-3367-5
Type
conf
DOI
10.1109/ISNFS.1996.603831
Filename
603831
Link To Document