Title :
Emergence of learning: an approach to coping with NP-complete problems in learning
Author :
Lu, Bao-Liang ; Ichikawa, Michinori
Author_Institution :
Lab. for Brain-Operative Device, RIKEN, Wako, Japan
Abstract :
Various theoretical results show that learning in conventional feedforward neural networks such as multilayer perceptrons is NP-complete. In this paper we show that learning in min-max modular (M 3) neural networks is tractable. The key to coping with NP-complete problems in M3 networks is to decompose a large-scale problem into a number of manageable, independent subproblems and to make the learning of a large-scale problem emerge from the learning of a number of related small subproblems
Keywords :
character recognition; computational complexity; feedforward neural nets; learning (artificial intelligence); minimisation; multilayer perceptrons; NP-complete problems; character recognition; computational complexity; feedforward neural networks; large-scale problem; min-max modular; minimisation; multilayer perceptrons; supervised learning; Biological neural networks; Computational complexity; Feedforward neural networks; Large-scale systems; Management training; Multi-layer neural network; NP-complete problem; Neural networks; Supervised learning; Training data;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.860766
