DocumentCode
3250238
Title
A smart algorithm for incremental learning
Author
Wang, Eric Hsiu-Chun ; Kuh, Anthony
Author_Institution
Dept. of Eng.-Econ. Syst., Stanford Univ., CA, USA
Volume
3
fYear
1992
fDate
7-11 Jun 1992
Firstpage
121
Abstract
Incremental learning algorithms not only adjust interconnection weights, but also change the network architecture by adding hidden nodes at the network. The capabilities of these incremental learning algorithms are examined. Four different incremental learning algorithms have been simulated for a variety of learning tasks. To improve the performance of the incremental learning algorithms, a new perceptron learning algorithm is proposed, the smart algorithm, to find the near-optimal set of weights at each node. The simulation results show that the smart algorithm improves the performance of these incremental learning algorithms. Among the four algorithms examined, the global algorithm performed the best
Keywords
learning (artificial intelligence); neural nets; incremental learning; interconnection weights; near-optimal weights set; perceptron learning algorithm; smart algorithm; Bayesian methods; Computer architecture; Curve fitting; Feedforward neural networks; Feedforward systems; Iterative algorithms; Neural networks; Polynomials; Systems engineering and theory; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.227182
Filename
227182
Link To Document