NDLI: Evaluation on life satisfaction of left-behind junior high school children based on LVQ network

Content Provider	IEEE Xplore Digital Library
Author	Gan Yuan-yuan
Copyright Year	2012
Description	Author affiliation: Psychology Department, Nanjing Normal University, China (Gan Yuan-yuan)
Abstract	A conceptually new approach to evaluating on life satisfaction of left-behind junior high school children using learning vector quantization neural network was proposed. The paper gives an introduction of learning vector quantization and discussed how this technique can be applied to evaluate on life satisfaction. The results indicated the following: Choosing the proper training samples, it is appropriate to evaluate on the left-behind children's life satisfaction by the LVQ neural network. According to the training samples' total scores of the six subscales and the corresponding grades information of their life satisfaction level, the life satisfaction grades of left-behind children were estimated accurately. For it is not necessary to evaluate their life satisfaction from six aspects such as school, school work, family, environment, friendship, and freedom in the process of estimating their total level of life satisfaction, the workload of evaluating on life satisfaction are sharp shorten. So the learning vector quantization neural network as an new approach evaluating on life satisfaction of left-behind junior high school children was effective, reliable, and with less labor and time.
Starting Page	405
Ending Page	408
File Size	248698
Page Count	4
File Format	PDF
ISBN	9781457721304
ISSN	21579563
e-ISBN	9781457721335
DOI	10.1109/ICNC.2012.6234755
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training left-behind children Vector quantization Psychology Correct rates of prediction learning vector quantization Educational institutions Vectors life satisfaction classification neural network Biological neural networks
Content Type	Text
Resource Type	Article

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