NDLI: Self-enhancement learning: Self-supervised and target-creating learning

Content Provider	IEEE Xplore Digital Library
Author	Kamimura, R.
Copyright Year	2009
Description	Author affiliation: IT Education Center of Tokai University, Japan (Kamimura, R.)
Abstract	In this paper, we propose a new learning method called “self-enhancement learning.” In this model, a network enhances its state by itself, and this enhanced state is to be imitated by another state of the network. The word “target” in our model means that a target is created spontaneously by a network, which must try to attain the target. Enhancement is realized by changing the Gaussian width or enhancement parameter. With different enhancement parameters, we can set up the different states of a network. In particular, we set up an enhanced and a relaxed state, and the relaxed state tries to imitate the enhanced state as much as possible. To demonstrate the effectiveness of this method, we apply the self-enhancement learning to the SOM. For this purpose, we introduce collectiveness into an enhanced state in which all neurons collectively respond to input patterns. Then, this enhanced and collective state should be imitated by the other non-enhanced and relaxed state. We applied the method to the Iris problem. Experimental results showed that the U-matrices obtained were significantly similar to those produced by the conventional SOM. However, much better performance could be obtained in terms of quantitative and topological errors. The experimental results suggest the possibility for the self-enhancement learning to be applied to many different neural network models.
Starting Page	1503
Ending Page	1509
File Size	1778128
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178677
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Neurons Temperature Machine learning Entropy Cooling Learning systems Iris Semisupervised learning Probability
Content Type	Text
Resource Type	Article

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