DocumentCode
1683559
Title
Knowledge enhancement and reuse with radial basis function networks
Author
Ghosh, Joydeep ; Nag, Arindam C.
Author_Institution
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1322
Lastpage
1327
Abstract
Presents a technique for enhancing an RBFN when provided with additional information in the form of new features, without retraining or resorting to the original features. The proposed technique improves the learning speed as well as network performance as compared to a network that is trained from scratch. We also present a method of reusing knowledge embedded in an RBFN for initializing another RBFN to be trained on a related problem. Both methods have several real-life applications
Keywords
learning (artificial intelligence); pattern classification; radial basis function networks; features; knowledge enhancement; knowledge reuse; learning speed; network performance; radial basis function networks; Data mining; Feature extraction; Intelligent networks; Radial basis function networks; Remote sensing; Sensor phenomena and characterization; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007686
Filename
1007686
Link To Document