Đang chuẩn bị liên kết để tải về tài liệu:
Data Mining and Knowledge Discovery Handbook, 2 Edition part 76

Data Mining and Knowledge Discovery Handbook, 2 Edition part 76. 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. | 730 Ricardo Vilalta Christophe Giraud-Carrier and Pavel Brazdil Metal. A Meta-Learning Assistant for Providing User Support in Machine Learning and Data Mining 1998. Michie D. Spiegelhalter D. J. Taylor C.C. Machine Learning Neural and Statistical Classification. England Ellis Horwood 1994. Nakhaeizadeh G. Schnabel A. Development of Multi-criteria Metrics for Evaluation of Data-mining Algorithms. In Proceedings of the Third International Conference on Knowledge Discovery and Data-Mining 1997. Paterson I. New Models for Data Envelopment Analysis Measuring Efficiency with the VRS Frontier. Economics Series No. 84 Institute for Advanced Studies Vienna 2000. Peng Y. Flach P. Brazdil P. Soares C. Decision Tree-Based Characterization for MetaLearning. In ECML PKDD 02 Workshop on Integration and Collaboration Aspects of Data Mining Decision Support and Meta-Learning 111-122. University of Helsinki 2002. Pfahringer B. Bensusan H. Giraud-Carrier C. Meta-learning by Landmarking Various Learning Algorithms. In Proceedings of the Seventeenth International Conference on Machine Learning 2000. Pratt L. Thrun S. Second Special Issue on Inductive Transfer. Machine Learning 28 1997. Pratt S. Jennings B. A Survey of Connectionist Network Reuse Through Transfer. In Learning to Learn Chapter 2 19-43 Kluwer Academic Publishers MA 1998. Rokach L. Averbuch M. and Maimon O. Information retrieval system for medical narrative reports. Lecture notes in artificial intelligence 3055. pp. 217-228 Springer-Verlag 2004 . Schmidhuber J. Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability. Proceedings of the Twelve International Conference on Machine Learning 488-49 Morgan Kaufman 1995. Skalak D. Prototype Selection for Composite Nearest Neighbor Classifiers. PhD thesis University of Massachusetts Amherst 1997. Soares C. Brazdil P Zoomed Ranking Selection of Classification Algorithms Based on Relevant Performance Information. In Proceedings of the Fourth European