Connectionism isn't just for cognitive science: neural networks as methodological tools.
Neural networks are presented as a complementary methodological tool to common statistical methods for certain types of classification problems. Prior research has suggested that neural networks can classify cases more accurately than many commonly used statistical techniques in situations where a d...
| Publicado en: | Psychological Record Vol. 51; no. 1; pp. 3 - 19 |
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| Autores principales: | , |
| Formato: | Artículo |
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Psychological Record
Winter2001
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| 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=507729455&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507729455 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: Winter2001 vid: 51 iid: 1 pid: 761 pub: Psychological Record artinfo: ui: 507729455 ppf: 3 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 838KB tig: atl: Connectionism isn't just for cognitive science: neural networks as methodological tools. aug: au: McMillen, Robert Henley, Tracy su: Artificial neural networks Regression analysis Risk -- Mathematical models Categorization (Psychology) Psychological techniques Psychology -- Statistical methods sug: subj: Artificial neural networks Regression analysis Risk -- Mathematical models Categorization (Psychology) Psychological techniques Psychology -- Statistical methods ab: Neural networks are presented as a complementary methodological tool to common statistical methods for certain types of classification problems. Prior research has suggested that neural networks can classify cases more accurately than many commonly used statistical techniques in situations where a data set does not fully meet the required assumptions, there are missing data, or there is a large amount of variance in several of the variables. There is also evidence to suggest that neural networks are a useful interpretive tool in these situations. Several neural networks were applied to a classification problem involving a problematic data set. The analyses were compared to a series of logistic regressions. Initially the logistic regression models provided greater predictive accuracy than the neural network models. However, as the data sets became more problematic, the accuracy of the neural network models surpassed that of the logistic regression models. It was concluded that neural networks are most useful as classification tools when insight and explanation are of less interest than predictive accuracy in problematic data sets. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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