HIERARCHICAL ARTIFICIAL NEURAL NETWORKS FOR RECOGNIZING HIGH SIMILAR LARGE DATA SETS
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 arc 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 teaming 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.
Similarity search SOM LVQ Recognition
YEN-LING LU CHIN-SHYURNG FAHN
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
1930-1935
2007-08-19（万方平台首次上网日期，不代表论文的发表时间）