Đang chuẩn bị liên kết để tải về tài liệu:
Data Mining and Knowledge Discovery Handbook, 2 Edition part 26

Data Mining and Knowledge Discovery Handbook, 2 Edition part 26. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 230 Richard A. Berk Dasu T. and T. Johnson 2003 Exploratory Data Mining and Data Cleaning. New York John Wiley and Sons. Christianini N and J. Shawe-Taylor. 2000 Support Vector Machines. Cambridge England Cambridge University Press. Fan J. and I. Gijbels. 1996 Local Polynomial Modeling and its Applications. New York Chapman Hall. Friedman J. Hastie T. and R. Tibsharini 2000 . Additive Logistic Regression A Statistical View of Boosting with discussion . Annals of Statistics 28 337-407. Freund Y. and R. Schapire. 1996 Experiments with a New Boosting Algorithm Machine Learning Proceedings of the Thirteenth International Conference 148-156. San Francisco Morgan Freeman Gigi A. 1990 Nonlinear Multivariate Analysis. New York John Wiley and Sons. Hand D. Manilla H. and P Smyth 2001 Principle of Data Mining. Cambridge Massachusetts MIT Press. Hastie T.J. and R.J. Tibshirani. 1990 Generalized Additive Models. New York Chapman Hall. Hastie T. Tibshirani R. and J. Friedman 2001 The Elements of Statistical Learning. New York Springer-Verlag. LeBlanc M. and R. Tibshirani 1996 Combining Estimates on Regression and Classification. Journal of the American Statistical Association 91 1641-1650. Loader C. 1999 Local Regression and Likelihood. New York Springer-Verlag. Loader C. 2004 Smoothing Local Regression Techniques in J. Gentle W. Hardle and Y. Mori Handbook of Computational Statistics. NewYork Springer-Verlag. Mocan H.N. and K. Gittings 2003 Getting off Death Row Commuted Sentences and the Deterrent Effect of Capital Punishment. Revised version of NBER Working Paper No. 8639 and forthcoming in the Journal of Law and Economics. Mojirsheibani M. 1999 Combining Classifiers vis Discretization. Journal of the American Statistical Association 94 600-609. Reunanen J. 2003 Overfitting in Making Comparisons between Variable Selection Methods. Journal of Machine Learning Research 3 1371-1382. Sutton R.S. and A.G. Barto. 1999 . Reinforcement Learning. Cambridge Massachusetts MIT Press. .