Classification and Regression via Integer Optimization

References

• Arthanari T. S., Dodge Y.Mathematical Programming in Statistics (1993) (Wiley Classics Library Edition, Wiley–Interscience, New York) Google Scholar
• Bennett K. P., Blue J. A. Optimal decision trees. (1984) . R.P.I. Math Report 214, Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NYGoogle Scholar
• Bertsimas D., Shioda R. An algorithm for cardinality constraint quadratic optimization. (2004) . In reviewGoogle Scholar
• Bienstock D. Computational study of families of mixed-integer quadratic programming problems. Math. Programming (1996) 74:121–140Crossref, Google Scholar
• Bousquet O., Elisseeff A. Stability and generalization. J. Machine Learn. Res. (2002) 2:445–498Google Scholar
• Breiman L., Friedman J., Olshen R., Stone C.Classification and Regression Trees (1984) (Wadsworth International, Belmont, CA) Google Scholar
• Friedman J. Multivariate adaptive regression splines. Ann. Statist. (1991) 9(1):1–67Crossref, Google Scholar
• Friedman J. H. Stochastic gradient boosting. (1999a) . http://www-stat.stanford.edu/∼jhf/ftp/stobst.psGoogle Scholar
• Friedman J. H. Greedy function approximation: A gradient boosting machine. (1999b) . http://www-stat.stanford.edu/∼jhf/ftp/trebst.psGoogle Scholar
• Friedman J. H. Getting started with MART in R. (2002) . http://www-stat.stanford.edu/∼jhf/r-mart/tutorial/tutorial.pdfGoogle Scholar
• Hastie T., Tibshirani R., Friedman J.The Elements of Statistical Learning (2001) (Springer-Verlag, New York) Crossref, Google Scholar
• ILOG CPLEX 7.1 Reference Guide (2001) . ILOG CPLEX Division, Incline Village, NVGoogle Scholar
• Johnson R. A., Wichern D. W.Applied Multivariate Statistical Analysis (1998) 4th ed.(Prentice-Hall, Englewood Cliffs, NJ) Google Scholar
• Kim H., Loh W. Y. CRUISE User Manual. (2000) . Technical Report 989, Department of Statistics, University of Wisconsin, Madison, WIGoogle Scholar
• Kim H., Loh W. Y. Classification tree with unbiased multiway splits. J. Amer. Statist. Assoc. (2001) 96:598–604Crossref, Google Scholar
• Loh W. Y., Shih Y. Split selection methods for classification trees. Statistica Sinica (1997) 7:815–840Google Scholar
• Mangasarian O. L. Linear and nonlinear separation of patterns by linear programming. Oper. Res. (1965) 13(3):444–452Link, Google Scholar
• Quinlan R.C4.5: Programs for Machine Learning (1993) (Morgan Kaufman, San Mateo, CA) Google Scholar
• Ridgeway G. Generalized boosted models: A guide to the gbm package. (2005) . http://i-pensieri.com/gregr/papers/gbm-vignette.pdfGoogle Scholar
• Rifkin R. Everything old is new again: A fresh look at historical approaches in machine learning. (2002) . Ph.D. thesis, Electrical Engineering and Comuter Science and Operations Research, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
• Rousseeuw P. J., Leroy A. M.Robust Regression and Outlier Detection (1987) (Wiley, New York) Crossref, Google Scholar
• Ryan T. P.Modern Regression Methods (1997) (Wiley Series in Probability and Statistics, New York) Google Scholar
• Vapnik V.The Nature of Statistical Learning Theory (1999) (Springer-Verlag, New York) Google Scholar