DocumentCode :
856662
Title :
A machine learning method for generation of a neural network architecture: a continuous ID3 algorithm
Author :
Cios, Krzysztof J. ; Liu, Ning
Author_Institution :
Dept. of Electr. Eng., Toledo Univ., OH, USA
Volume :
3
Issue :
2
fYear :
1992
fDate :
3/1/1992 12:00:00 AM
Firstpage :
280
Lastpage :
291
Abstract :
The relation between the decision trees generated by a machine learning algorithm and the hidden layers of a neural network is described. A continuous ID3 algorithm is proposed that converts decision trees into hidden layers. The algorithm allows self-generation of a feedforward neural network architecture. In addition, it allows interpretation of the knowledge embedded in the generated connections and weights. A fast simulated annealing strategy, known as Cauchy training, is incorporated into the algorithm to escape from local minima. The performance of the algorithm is analyzed on spiral data
Keywords :
decision theory; entropy; learning systems; neural nets; simulated annealing; trees (mathematics); Cauchy training; architecture generation; continuous ID3 algorithm; decision trees; feedforward neural network architecture; hidden layers; machine learning method; self-generation; simulated annealing; spiral data; Algorithm design and analysis; Data analysis; Decision trees; Feedforward neural networks; Learning systems; Machine learning algorithms; Neural networks; Performance analysis; Simulated annealing; Spirals;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.125869
Filename :
125869
Link To Document :
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