Data Mining and Knowledge Discovery Handbook, 2 Edition part 86

Data Mining and Knowledge Discovery Handbook, 2 Edition part 86. 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. | 830 Moty Ben-Dov and Ronen Feldman Hayes P J. Knecht L. E. and Cellio M. J. 1988 . A news story categorization system Proceedings of ANLP-88 2nd Conference on Applied Natural Language Processing 9-17. Austin US Association for Computational Linguistics Morristown US. Hayes P J. and S. P Weinstein 1990 . Construe Tis a system for contentbased indexing of a database of news stories. Proceedings of IAAI-90 2nd Conference on Innovative Applications of Artificial Intelligence. AAAI Press Menlo Park US 49-66. Hearst M. A. 1999 . Untangling Text Data Mining. Proceedings of ACL 99 the 37th Annual Meeting of the Association for Computational Linguistics University of Maryland. Hobbs J. R. Appelt D. E. John Bear D. I. Kameyama M. and Tyson M. 1992 . FASTUS A System for Extracting Information from Text. Paper presented at the Human Language Technology. Hopkins J. and J. Cui 2004 . Maximum Entropy Modeling in Sparse Semantic Tagging NSF grant numbers IIS- 0121285. Huffman S. B. 1995 . Learning information extraction patterns from examples. Learning for Natural Language Processing 246-260. Ittner D. J. Lewis D. D. and Ahn D. D. 1995 . Text categorization of low quality images Proceedings of SDAIR-95 4th Annual Symposium on Document Analysis and Information Retrieval 301-315. Las Vegas US. Iwayama M. and T. Tokunaga 1994 . A Probabilistic Model for Text Categorization Based on a Single Random Variable with Multiple Values. In Proceedings of the 4th Conference on Applied Natural Language Processing. Jacobs P. 1992 . Joining Statistics with NLP for Text Categorization. In Proceedings of the 3rd Conference on Applied Natural Language Processing. Jo T. C. 1999 . Text categorization with the concept of fuzzy set of informative keywords. Proceedings of FUZZ-IEEE 99 IEEE International Conference on Fuzzy Systems. Seoul KR IEEE Computer Society Press Los Alamitos US 609-614. Joachims T. 1998 . Text categorization with support vector machines learning with many relevant features. .