Seminars in Oncology
Volume 37, Issue 1 , Pages 65-68 , February 2010

Learning Curves in Classification With Microarray Data

  • Kenneth R. Hess

      Affiliations

    • Corresponding Author InformationAddress correspondence to Kenneth R. Hess, PhD, Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston TX, 77030
  • ,
  • Caimiao Wei

References 

  1. Ramsay CR, Grant AM, Wallace SA, Garthwaite PH, Monk AF, Russell IT. Statistical assessment of the learning curves of health technologies. Health Technol Assess. 2001;5:1–79
  2. Ritter FE, Schooler LJ. The learning curve. In:  Smelser NJ,  Baltes PB editor. International encyclopedia of the social and behavioral sciences (IESBS). New York: Elsevier Science; 2001;p. 8602–8605
  3. Thorstone LL. The learning curve equation. Psychol Monogr. 1919;26:51
  4. Yelle LE. The learning curve: historical review and comprehensive survey. Decision Sci. 1979;10:302–327
  5. Kadie CM. Quantifying the value of constructive induction, knowledge, and noise filtering on inductive learning. In: Proceeding of the 8th Machine Learning Workshop. 1991;p. 153–157
  6. Provost F, Jensen D, Oates T. Efficient progressive sampling. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99). 1999;p. 23–32
  7. Gu B, Feifang H, Liu H. Modeling classification performance for large data sets: an empirical study. Research Paper Republic of Singapore: National University of Singapore; 2000;http:www.nus.edu.sg
  8. Mukherjee S, Tamayo P, Rogers S, et al. Estimating dataset size requirements for classifying DNA microarray data. J Comput Biol. 2003;10:119–142
  9. Dudoit S, Fridlyand J, Speed T. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc. 2002;97:77–87
  10. Pepe MS. The statistical evaluation of medical tests for classification and prediction. In: Oxford Statistical Science Series 28. New York: Oxford University Press; 2003;p. 66–129
  11. Huet S, Bouvier A, Poursat MA, Jolivet E. Statistical tools for nonlinear regression. In: A practical guide with S-PLUS and R examples. Ed. 2. New York: Springer-Verlag; 2004;p. 137–139

PII: S0093-7754(09)00227-9

doi: 10.1053/j.seminoncol.2009.12.002

Seminars in Oncology
Volume 37, Issue 1 , Pages 65-68 , February 2010