NDLI: Hierarchical Artificial Neural Networks for Recognizing High Similar Large Data Sets

Content Provider	IEEE Xplore Digital Library
Author	Yen-Ling Lu Chin-Shyurng Fahn
Copyright Year	2007
Description	Author affiliation: Nat. Taiwan Univ. of Sci. & Technol., Taipei (Yen-Ling Lu; Chin-Shyurng Fahn)
Abstract	This paper proposes a hierarchical artificial neural network for recognizing high similar large data sets. It is usually required to classify large data sets with high similar characteristics in many applications. Analyzing and identifying those data is a laborious task when the methods adopted are primarily based on visual inspection. In many field applications, data sets are measured and recorded continuously using automatic monitoring equipments. Therefore, a large amount of data can be collected, and manual inspection has become an unsuitable approach to recognizing those data. This proposed hierarchical neural network integrates self-organizing feature map (SOM) networks and learning vector quantization (LVQ) networks. The SOM networks provide an approximate method for computing the input vectors in an unsupervised manner. Then the computation of the SOM may be viewed as the first stage of the proposed hierarchical network. The second stage is provided by the LVQ networks based on a supervised learning technique that uses class information to improve the quality of the classifier from the first stage. The multistage hierarchical network attempts to factorize the overall input vector into a number of small groups, each of which requires very little computation. Consequently, by use of the proposed network, the loss in accuracy can be small.
Starting Page	1930
Ending Page	1935
File Size	633832
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370463
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Computer networks Inspection Nearest neighbor searches Tree data structures Machine learning Cybernetics Computer science Data engineering Electronic mail Recognition Similarity search SOM LVQ
Content Type	Text
Resource Type	Article

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