fbpx
Wikipedia

Optimal discriminant analysis and classification tree analysis

January 01, 1970

Optimal Discriminant Analysis (ODA)[1] and the related classification tree analysis (CTA) are exact statistical methods that maximize predictive accuracy. For any specific sample and exploratory or confirmatory hypothesis, optimal discriminant analysis (ODA) identifies the statistical model that yields maximum predictive accuracy, assesses the exact Type I error rate, and evaluates potential cross-generalizability. Optimal discriminant analysis may be applied to > 0 dimensions, with the one-dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA. Optimal discriminant analysis is an alternative to ANOVA (analysis of variance) and regression analysis.

See also edit

References edit

  1. ^ Provider: John Wiley & Sons, Ltd Content:text/plain; charset="UTF-8" TY - JOUR AU - Yarnold, Paul R. AU - Soltysik, Robert C. TI - Theoretical Distributions of Optima for Univariate Discrimination of Random Data* JO - Decision Sciences VL - 22 IS - 4 PB - Blackwell Publishing Ltd SN - 1540-5915 UR - https://dx.doi.org/10.1111/j.1540-5915.1991.tb00362.x DO - 10.1111/j.1540-5915.1991.tb00362.x SP - 739 EP - 752 KW - Discrete Programming KW - Linear Statistical Models KW - Mathematical Programming KW - and Statistical Techniques PY - 1991 ER -1.tb00362.x

Notes edit

  • Yarnold, Paul R.; Soltysik, Robert C. (2004). . American Psychological Association. ISBN 978-1-55798-981-9. Archived from the original on 2008-11-23. Retrieved 2009-09-11.
  • Fisher, R. A. (1936). "The Use of Multiple Measurements in Taxonomic Problems". Annals of Eugenics. 7 (2): 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x. hdl:2440/15227.
  • Martinez, A. M.; Kak, A. C. (2001). "PCA versus LDA" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 23 (2): 228–233. doi:10.1109/34.908974.[permanent dead link]
  • Mika, S.; et al. (1999). "Fisher discriminant analysis with kernels". Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468). pp. 41–48. CiteSeerX 10.1.1.35.9904. doi:10.1109/NNSP.1999.788121. ISBN 978-0-7803-5673-3. S2CID 8473401.{{cite book}}: CS1 maint: date and year (link)

External links edit

  • LDA tutorial using MS Excel
  • , which has many useful mathematical definitions.

January 01, 1970
optimal, discriminant, analysis, classification, tree, analysis, this, article, includes, list, references, related, reading, external, links, sources, remain, unclear, because, lacks, inline, citations, please, help, improve, this, article, introducing, more,. This article includes a list of references related reading or external links but its sources remain unclear because it lacks inline citations Please help improve this article by introducing more precise citations September 2009 Learn how and when to remove this message Optimal Discriminant Analysis ODA 1 and the related classification tree analysis CTA are exact statistical methods that maximize predictive accuracy For any specific sample and exploratory or confirmatory hypothesis optimal discriminant analysis ODA identifies the statistical model that yields maximum predictive accuracy assesses the exact Type I error rate and evaluates potential cross generalizability Optimal discriminant analysis may be applied to gt 0 dimensions with the one dimensional case being referred to as UniODA and the multidimensional case being referred to as MultiODA Optimal discriminant analysis is an alternative to ANOVA analysis of variance and regression analysis Contents 1 See also 2 References 3 Notes 4 External linksSee also editData mining Decision tree Factor analysis Linear classifier Logit for logistic regression Machine learning Multidimensional scaling Perceptron Preference regression Quadratic classifier StatisticsReferences edit Provider John Wiley amp Sons Ltd Content text plain charset UTF 8 TY JOUR AU Yarnold Paul R AU Soltysik Robert C TI Theoretical Distributions of Optima for Univariate Discrimination of Random Data JO Decision Sciences VL 22 IS 4 PB Blackwell Publishing Ltd SN 1540 5915 UR https dx doi org 10 1111 j 1540 5915 1991 tb00362 x DO 10 1111 j 1540 5915 1991 tb00362 x SP 739 EP 752 KW Discrete Programming KW Linear Statistical Models KW Mathematical Programming KW and Statistical Techniques PY 1991 ER 1 tb00362 xNotes editYarnold Paul R Soltysik Robert C 2004 Optimal Data Analysis American Psychological Association ISBN 978 1 55798 981 9 Archived from the original on 2008 11 23 Retrieved 2009 09 11 Fisher R A 1936 The Use of Multiple Measurements in Taxonomic Problems Annals of Eugenics 7 2 179 188 doi 10 1111 j 1469 1809 1936 tb02137 x hdl 2440 15227 Martinez A M Kak A C 2001 PCA versus LDA PDF IEEE Transactions on Pattern Analysis and Machine Intelligence 23 2 228 233 doi 10 1109 34 908974 permanent dead link Mika S et al 1999 Fisher discriminant analysis with kernels Neural Networks for Signal Processing IX Proceedings of the 1999 IEEE Signal Processing Society Workshop Cat No 98TH8468 pp 41 48 CiteSeerX 10 1 1 35 9904 doi 10 1109 NNSP 1999 788121 ISBN 978 0 7803 5673 3 S2CID 8473401 a href Template Cite book html title Template Cite book cite book a CS1 maint date and year link External links editLDA tutorial using MS Excel IMSL discriminant analysis function DSCRM which has many useful mathematical definitions Retrieved from https en wikipedia org w index php title Optimal discriminant analysis and classification tree analysis amp oldid 1168798801, wikipedia, wiki, book, books, library,

article

, read, download, free, free download, mp3, video, mp4, 3gp, jpg, jpeg, gif, png, picture, music, song, movie, book, game, games.