DocumentCode
396659
Title
Perceptron learning in the domain of graphs
Author
Jain, Brijnesh J. ; Wysotzki, Fritz
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Technische Univ. Berlin, Germany
Volume
3
fYear
2003
fDate
20-24 July 2003
Firstpage
1993
Abstract
We develop a new mathematical framework, which embeds weighted graphs into quasi metric spaces. This concept establishes a theoretical basis to apply neural learning machines for structured data. To exemplarily illustrate the applicability of metric graph spaces, we propose and analyze a perceptron learning algorithm for graphs in its primal and dual form.
Keywords
graph theory; graphs; learning (artificial intelligence); perceptrons; graphs domain; metric graph spaces; neural learning machines; perceptron learning algorithm; quasi metric spaces; structured data; weighted graphs; Algorithm design and analysis; Classification algorithms; Computer science; Electronic mail; Extraterrestrial measurements; Linear discriminant analysis; Machine learning; Neural networks; Training data; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223713
Filename
1223713
Link To Document