tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 125

Data Mining and Knowledge Discovery Handbook, 2 Edition part 125. 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. | 1220 Nissan Levin and Jacob Zahavi Heckman J. Sample Selection Bias as a Specification Error Econometrica Vol. 47 No. 1 pp. 153-161 1979. Gilbert A. and Churchill Jr. Marketing Research. Seventh edition The Dryden Press 1999. George . The Variable Selection Problem University of Texas Austin 2000. Herz F. Ungar L. and Labys P. A Collaborative Filtering System for the Analysis of Consumer Data. Univ. of Pennsylvania Philadelphia 1997. Hodges . Jr. The Significance Probability of the Smirnov Two-Sample Test Arkiv for Matematik 3 469 -486 1957. Kass G. An Exploratory Technique for Investigating large Quantities of Categorical Data Applied Statistics 29 1983. Kohonen K. Makisara K. Simula O. and Kangas J. Artificial Networks. Amsterdam 1991. Lauritzen . The EM algorithm for Graphical Association Models with Missing Data. Computational Statistics and Data Analysis 19 191-201 1995. Long . Regression Models for Categorical and Limited Dependent Variables Sage Publications Thousand Oaks CA 1997. Lambert . The Distribution and Redistribution of Income. Manchester University Press. 1993. Levin N. and Zahavi J. Segmentation Analysis with Managerial Judgment Journal of Direct Marketing Vol. 10 pp. 28-47 1996. Levin N. and Zahavi J. Applying Neural Computing to Target Marketing The Journal of Direct Marketing Vol. 11 No. 1 pp. 5-22 1997a. Levin N. and Zahavi J. Issues and Problems in Applying Neural Computing to Target Marketing The Journal of Direct marketing Vol. 11 No. 4 pp. 63-75 1997b. Miller A. Subset Selection in Regression Chapman and Hall London 2002. Quinlan . Induction of Decision Trees Machine Learning 1 pp. 81-106 1986. Quinlan . Program for Machine Learning CA. Morgan Kaufman Publishing 1993. Rumelhart . McClelland . and Williams . Learning Internal Representation by Error Propagation in Parallel Distributed Processing Exploring the Microstructure of Cognition Rumelhart . McClelland . and the PDP Researcg Group eds. MIT .