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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007686
