NDLI: An Information Gain Technique for Acceleration of Convergence of Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Jearanaitanakij, K. Pinngern, O.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Eng., King Mongkut's Inst. of Technol., Bangkok (Jearanaitanakij, K.; Pinngern, O.)
Abstract	This paper presents an application of information gain to accelerate the convergence time of artificial neural networks (ANNs). We improve Hagiwara's convergence acceleration algorithm by applying information gain to it. The first step of our proposed technique is to calculate information gains of all features (or attributes) in training data and pass those gains through all hidden units in the next layer. During the training process, the algorithm monitors sum-squared error at the output layer. When the variation of sum-squared error becomes small, the worst hidden unit is detected. Next, all the weights connected to the worst hidden unit are reset to random values within the appropriate ranges. These ranges are determined by the propagated information gain of the worst hidden unit. Then, the network is retrained. When the number of weight resetting trials reaches a certain number, a new hidden unit is added to the network and the whole training process is repeated. Our experimental results on standard benchmarks show remarkable outputs in terms of convergence time
Starting Page	349
Ending Page	352
File Size	5201122
Page Count	4
File Format	PDF
ISBN	0780392833
DOI	10.1109/ICICS.2005.1689065
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-06
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acceleration Convergence Artificial neural networks Entropy Neural networks Testing Computer networks Application software Training data Backpropagation classification Artificial Neural Network convergence acceleration information gain
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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