BEHAVIORAL AND PHYSIOLOGICAL NEURAL NETWORK ANALYSES: A COMMON PATHWAY TOWARD PATTERN RECOGNITION AND PREDICTION.
Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Mac...
| Publicado en: | Psychological Record Vol. 62; no. 4; pp. 579 - 598 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Fall2012
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=83237091&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 83237091 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00332933 PSD jtl: Psychological Record issn: 00332933 maglogo: N pubinfo: dt: Fall2012 vid: 62 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 83237091 10.1007/BF03395822 ppf: 579 ppct: 19 formats: fmt: @attributes: type: P size: 3.6MB tig: atl: BEHAVIORAL AND PHYSIOLOGICAL NEURAL NETWORK ANALYSES: A COMMON PATHWAY TOWARD PATTERN RECOGNITION AND PREDICTION. aug: au: Ninness, Chris Lauter, Judy L. Coffee, Michael Clary, Logan Kelly, Elizabeth Rumph, Marilyn Rumph, Robin Kyle, Betty Ninness, Sharon K. affil: Stephen F. Austin State University Angelina College su: Self-organizing maps Pattern perception Artificial neural networks Machine learning Breast cancer Cancer cells sug: subj: Self-organizing maps Pattern perception Artificial neural networks Machine learning Breast cancer Cancer cells keyword: factor analysis logistic regression pattern recognition prediction principal components analysis Self-Organizing Map factor analysis logistic regression pattern recognition prediction principal components analysis Self-Organizing Map ab: Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study were composed of votes for each U.S. Representative on 16 key items during a particular legislative session. Experiment 2 employed a dataset developed in our human neuroscience laboratory and focused on the effects of sympathetic nervous system arousal on cardiac and inner-ear physiology. Experiment 3 employed the well-known Wisconsin Breast Cancer dataset, which was used to develop a sensitive, automated diagnostic method of distinguishing between malignant and benign cells. We suggest that the SOM is capable of identifying cohesive patterns of nonlinear measurements that would be difficult to identify using traditional linear data reduction procedures and that neural networks will be increasingly valuable in the analysis of a wide range of complex behaviors. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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