Authors:
Seiya Satoh
and
Ryohei Nakano
Affiliation:
Chubu University, Japan
Keyword(s):
Multilayer Perceptron, Learning Method, Singular Region, Reducibility Mapping.
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
;
Theory and Methods
Abstract:
In a search space of a multilayer perceptron having J hidden units, MLP(J), there exist flat areas called singular
regions that cause serious stagnation of learning. Recently a method called SSF1.3 utilizing singular regions
has been proposed to systematically and stably find excellent solutions. SSF1.3 starts search from a search
space of MLP(1), increasing J one by one. This paper proposes SSF2 that performs MLP search by utilizing
singular regions with J changed bidirectionally within a certain range. The proposed method was evaluated
using artificial and real data sets.