Title :
Recent developments in supervised learning
Author :
Karayiannis, Nicolaos B.
Author_Institution :
Dept. of Electr. Eng., Houston Univ., TX, USA
Abstract :
The author presents a generalized criterion proposed recently for the fast training of feedforward neural networks. A strategy is also described which allows the development of learning algorithms capable of determining the architecture of feedforward neural networks, i.e., the number of hidden layers and the number of hidden units per layer, while performing their training. It was experimentally verified that the proposed algorithms provide the simplest possible network for a given training task
Keywords :
feedforward neural nets; learning (artificial intelligence); feedforward neural networks; neural net architecture; neural net training; supervised learning; Approximation methods; Biological neural networks; Degradation; Feedforward neural networks; Feedforward systems; Intelligent networks; Multi-layer neural network; Neural networks; Supervised learning; Upper bound;
Conference_Titel :
Systems, Man and Cybernetics, 1992., IEEE International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-0720-8
DOI :
10.1109/ICSMC.1992.271744
