Authors:
Ryohei Nakano
;
Seiya Satoh
and
Takayuki Ohwaki
Affiliation:
Chubu University, Japan
Keyword(s):
Multilayer perceptron, Singular region, Learning method, Polynomial network, XOR problem.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
In a search space of multilayer perceptron having J hidden units, MLP(J), there exists a singular flat region created by the projection of the optimal solution of MLP(J-1). Since such a singular region causes serious slowdown for learning methods, a method for avoiding the region has been aspired. However, such avoiding does not guarantee the quality of the final solution. This paper proposes a new learning method which does not avoid but makes good use of singular regions to find a solution good enough for MLP(J). The potential of the method is shown by our experiments using artificial data sets, XOR problem, and a real data set.