On classification capability of neural networks: a case study with otoneurological data.
We investigated the capability of multilayer perceptron neural networks and Kohonen neural networks to recognize difficult otoneurological diseases from each other. We found that they are efficient methods, but the distribution of a learning set should be rather uniform. Also it is important that the number of learning cases is sufficient. If the two mentioned conditions are satisfied, these neural networks are similarly efficient as some other machine learning methods. The conditions are known in the theory of neural networks [1,2], but not often taken seriously in practice. Both networks functioned as well, excluding the case with several input variables, where the Kohonen neural networks surpassed the perceptron.
Juhola
M
M
Department of Computer and Information Sciences, 33014 University of Tampere, Finland. mj@cs.uta.fi
Viikki
K
K
Laurikkala
J
J
Pyykkö
I
I
Kentala
E
E
D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D004427:Ear Diseases / Q000145:classification*; D006311:Hearing Disorders / Q000145:classification; D006801:Humans; D007759:Labyrinth Diseases / Q000145:classification; D016571:Neural Networks, Computer