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Visual Six Sigma: Making Data Analysis Lean by Leo Wright, Mia L. Stephens, Philip J. Ramsey, Marie A. Gaudard, Ian Cox

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9.10. Notes

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[] Olvi L. Mangasarian, W. Nick Street, and William H. Wolberg, "Breast Cancer Diagnosis and Prognosis via Linear Programming," Mathematical Programming Technical Report 94–10 (December 19, 1994): 1–9.

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[] For more detail on these characteristics, see W. Nick Street, William H. Wolberg, and Olvi L. Mangasarian, "Nuclear Feature Extraction for Breast Tumor Diagnosis," International Symposium on Electronic Imaging: Science and Technology 1905 (1993): 861–870.

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[] Mangasarian, Street, and Wolberg, "Breast Cancer Diagnosis and Prognosis via Linear Programming"; and Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (New York, NY: Springer, 2001), 108–111, 371–389.

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[] The validation and test sets are sometimes used in roles different from the ones we have described. We follow the convention proposed in: Christopher M. Bishop, Neural Networks for Pattern Recognition (New York, NY: Oxford University Press, 1995), 372; Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (New York, NY: Springer, 2001), 196; and B. D. Ripley, Pattern Recognition and Neural Networks (New York, NY: Cambridge University Press, 1996), 3–8.

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[] John P. Sall and Cathy Maahs-Fladung, "Trees, Neural Nets, PLS, I-Optimal Designs and Other New JMP® Version 5 Novelties," SUGI 27, http://www2.sas.com/proceedings/sugi27/p268-27.pdf (accessed ...

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