A statistical perspective on data mining

Future Generation Computer Systems

Volumn 13, Issue 2-3, 1997, Pages 117-134

  Hosking, Jonathan R M ^a   Pednault, Edwin P D ^a   Sudan, Madhu ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Classification; Frequentist inference; PAC learning; Statistical learning theory

Indexed keywords

COMPUTATION THEORY; LEARNING SYSTEMS; STATISTICAL METHODS;

DATA MINING; FREQUENTIST INFERENCE; VAPNIK'S STATISTICAL LEARNING THEORY;

DATA ACQUISITION;

EID: 0031272147     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0167-739x(97)00016-2     Document Type: Article

References (32)

1. D. Angluin, Computational learning theory: Survey and selected bibliography, in: Proc. 24th Ann. Symp. on Theory of Computing (ACM, New York, 1992) 351-369.
2. D.R. Cox and D.V. Hinkley, Theoretical Statistics (Chapman and Hall, London, 1986).
3. B. Efron, The Jackknife, The Bootstrap, and Other Resampling Plans, CBMS Monograph 38 (SIAM, Philadelphia, PA, 1981).
4. D.J. Hand, Discrimination and Classification (Wiley, Chichester, UK, 1981).
5. D. Haussler, Decision theoretic generalizations of the PAC learning model, in: Algorithmic Learning Theory, eds. S. Arikawa, S. Goto, S. Ohsuga and T. Yokomori (Springer, New York, 1990) 21-41.
6. P.J. Huber, Robust Statistics (Wiley, New York, 1981).
7. G. John, R. Kohavi and K. Pfleger, Irrelevant features and the subset selection problem, in: Machine Learning: Proc. 11th Int. Conf. (Morgan Kaufmann, San Mateo, CA, 1994) 121-129.
8. A.G. Journel and C.J. Huibregts, Mining Geostatistics (Academic Press, London, 1978).
9. M.J. Kearns and U.V. Vazirani, An Introduction to Computational Learning Theory (MIT Press, Cambridge, MA, 1994).
10. I. Kononenko and S.J. Hong, Attribute selection for modeling, Future Generation Computer Systems (1997).
11. M.C. Lovell, Data mining, Rev. Econom. Statist. 65 (1983) 1-12.
12. Y. Mansour, Lecture notes on learning theory, Available from (http://www.math.tau.ac.il/~mansour).
13. D. Michie, D.J. Spiegelhalter and C.C. Taylor, eds., Machine Learning, Neural and Statistical Classification (Ellis Horwood, Kernel Hempstead, UK, 1994).
14. A.J. Miller, Contribution to the discussion of "Regression, prediction and shrinkage" by J.B. Copas, J. Roy. Statist. Soc. Ser. B 45 (1983) 346-347.
15. J. Pearl, Causal diagrams for empirical research, Biometrika 82 (1995) 669-710.
16. B.D. Ripley, Comment on "Neural networks: A review from a statistical perspective" by B. Cheng and D.M. Titterington, Statist. Sci. 9 (1994) 45-48.
17. J. Rissanen, Stochastic Complexity in Statistical Inquiry (World Scientific, River Edge, NJ, 1989).
18. Y. Sakamoto, M. Ishiguro and G. Kitagawa, Akaike Information Criterion Statistics (Reidel, Dordrecht, 1986).
19. J.W. Tukey, Exploratory Data Analysis (Addison-Wesley, Reading, MA, 1977).
20. W. Vach, R. Rossner and M. Schumacher, Neural networks and logistic regression: Part II, Comput. Statist. Data Anal. 21 (1996) 683-701.
21. L. Valiant, A view of computational learning theory, in: Computation and Cognition: Proc. 1st NEC Research Symp. (SIAM, Philadelphia, PA, 1991) 32-51.
22. V.N. Vapnik, Estimation of Dependencies Based on Empirical Data (Springer, New York, 1982).
23. V.N. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 1995).
24. V.N. Vapnik, Statistical Learning Theory (Wiley, New York, 1997), to appear.
25. V.N. Vapnik and A.Ja. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities, Theory Probab. Appl. 16 (1971) 264-280; Originally published in: Doklady Akademii Nauk USSR 181 (1968).
26. V.N. Vapnik and A.Ja. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities, Theory Probab. Appl. 16 (1971) 264-280; Originally published in: Doklady Akademii Nauk USSR 181 (1968).
27. V.N. Vapnik and A.Ja. Chervonenkis, Necessary and sufficient conditions for the uniform convergence of means to their expectations, Theory Probab. Appl. 26 (1981) 532-553.
28. V.N. Vapnik and A.Ja. Chervonenkis, The necessary and sufficient conditions for consistency of the method of empirical risk minimization, Pattern Recognition and Image Anal., 1 (1991) 284-305; Originally published in: Yearbook of the Academy of Sciences of the USSR on Recognition, Classification, and Forecasting 2 (1989).
29. V.N. Vapnik and A.Ja. Chervonenkis, The necessary and sufficient conditions for consistency of the method of empirical risk minimization, Pattern Recognition and Image Anal., 1 (1991) 284-305; Originally published in: Yearbook of the Academy of Sciences of the USSR on Recognition, Classification, and Forecasting 2 (1989).
30. C.S. Wallace and P.R. Freeman, Estimation and inference by compact codes, J. Roy. Statist. Soc. Ser. B 49 (1987) 240-252.
31. S. Weisberg, Applied Regression Analysis, 2nd Ed. (Wiley, New York, 1985).
32. D. Wolpert, Stacked generalization, Neural Networks 5 (1992) 241-259.