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Description

The examples are complete and noise free. The examples highly simplified the problem. The attributes do not fully describe all the factors affecting the decision as to which type, if any, to fit. Notes: --This database is complete (all possible combinations of attribute-value pairs are represented). --Each instance is complete and correct. --9 rules cover the training set.

Related Papers

• Ke Wang and Shiyu Zhou and Ada Wai-Chee Fu and Jeffrey Xu Yu. Mining Changes of Classification by Correspondence Tracing. SDM. 2003. [link]
• Pedro Domingos. Knowledge Discovery Via Multiple Models. Intell. Data Anal, 2. 1998. [link]
• Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, 3. 1995. [link]
• Mehmet Dalkilic and Arijit Sengupta. A Logic-theoretic classifier called Circle. School of Informatics Center for Genomics and BioInformatics Indiana University. [link]
• Jeremy Kubica and Andrew Moore. Probabilistic Noise Identification and Data Cleaning. ICDM. 2003. [link]
• Anthony D. Griffiths and Derek Bridge. A Yardstick for the Evaluation of Case-Based Classifiers. Department of Computer Science, University of York. [link]
• J. Kent Martin and Daniel S. Hirschberg. Small Sample Statistics for Classification Error Rates I: Error Rate Measurements. Department of Information and Computer Science University of California, Irvine. 1996. [link]
• Christophe G. Giraud-Carrier and Tony Martinez. AN INCREMENTAL LEARNING MODEL FOR COMMONSENSE REASONING. Department of Computer Science Brigham Young University. [link]
• Christophe Giraud and Tony Martinez and Christophe G. Giraud-Carrier. University of Bristol Department of Computer Science ILA: Combining Inductive Learning with Prior Knowledge and Reasoning. 1995. [link]
• Jim Prentzas and Ioannis Hatzilygeroudis and Athanasios K. Tsakalidis. Updating a Hybrid Rule Base with New Empirical Source Knowledge. ICTAI. 2002. [link]
• Bob Ricks and Dan Ventura. Training a Quantum Neural Network. NIPS. 2003. [link]
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