The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 89-706
Full Name: Chih-Chien Yang
Position: Professor
Age: ON
Sex: Male
Address: 140 MenSheng Road, Taichung 403
Country: TAIWAN
Tel: +886 4 22183523
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E-mail address: noahyang@ntcu.edu.tw
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Title of the Paper: Supervised Learning Vector Quantization for Projecting Missing Weights of Hierarchical Neural Networks
Authors as they appear in the Paper: Cin-Ru Chen, Liang-Ting Tsai, Chih-Chien Yang
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Number of paper pages: 10
Abstract: A supervised learning vector quantization (LVQ) method is proposed in this paper to project stratified random samples to infer hierarchical neural networks. Comparing with two traditional methods, i.e., list-wise deletion (LWD), and non-amplified (NA), the supervised LVQ shows satisfying efficiencies and accuracies in simulation studies. The accomplishments of proposed LVQ method can be significant for sociological and psychological surveys in properly inferring the targeted populations with hierarchical neural network structure. In the numerical simulation study, successes of LVQ in projecting samples to infer the original population are further examined by experimental factors of sampling sizes, missing rates, and disproportion rates. The experimental design is to reflect practical research and under these conditions it shows the neural network approach is more accurate and reliable than its competitors.
Keywords: Neural Network, Learning Vector Quantization, Missing Weights, Stratified Structure, Simulation Study, Large Scale Data.
EXTENSION of the file: .pdf
Special (Invited) Session: A Neural Network Approach for Amplifying Random Samples to Stratified Psychometrical Population
Organizer of the Session: 642-201
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