Geometric decision rules for instance-based learning problems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3776 LNCS, Issue , 2005, Pages 60-69

  Bhattacharya, Binay ^a   Mukherjee, Kaustav ^a   Toussaint, Godfried ^b  

^a SIMON FRASER UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL GEOMETRY; DATA MINING; DATA STRUCTURES; DATABASE SYSTEMS; DECISION THEORY; GRAPH THEORY; PATTERN RECOGNITION; PROBLEM SOLVING;

GEOMETRIC DECISION RULES; INSTANCE-BASED LEARNING PROBLEMS; LOW ERROR RATES; TRAINING SETS;

LEARNING SYSTEMS;

EID: 33646719102     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11590316_9     Document Type: Conference Paper

References (13)

1. Binay Bhattacharya and Damon Kaller. Reference set thinning for the k-nearest neighbor decision rule. In Proceedings of the 14th International Conference on Pattern Recognition, volume 1, 1998.
2. Binay K. Bhattacharya. Application of computational geometry to pattern recognition problems. Ph.d. thesis, School of Computer Science, McGill University, 1982.
3. Henry Brighton and Chris Mellish. Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery, 6:153-172, 2002.
4. Luc Devroye, László Györfi, and Gábor Lugosi. A Probabilistic Theory of Pattern Recognition. Springer-Verlag New York, Inc., 1996.
5. Michael Houle. SASH: A spatial approximation sample hierarchy for similarity search. Tech. Report RT-0517, IBM Tokyo Research Laboratory, 2003.
6. Sanjeev R. Kulkarni, Gabor Lugosi, and Santosh S. Venkatesh. Learning pattern classification - a survey. IEEE Transactions on Information Theory, 44:2178-2206, 1998.
7. C. J. Merz and P. M. Murphy. UCI repository of machine learning database. Internet http://www.ics.uci.edu/mlearn/MLRepository.html, Department of Information and Computer Science, University of California.
8. Kaustav Mukherjee. Application of the gabriel graph to instance-based learning. M.sc. project, School of Computing Science, Simon Fraser University, 2004.
9. Demetri Psaltis, Robert R. Snapp, and Santosh S. Venkatesh. On the finite sample performance of the nearest neighbor classifier. IEEE Transactions on Information Theory, 40:820-837, 1994.
10. G. T. Toussaint, B. K. Bhattacharya, and R. S. Poulsen. The application of Voronoi diagrams to nonparametric decision rules. In Computer Science and Statistics: The Interface, pages 97-108, Atlanta, 1985.
11. G. T. Toussaint and R. S. Poulsen. Some new algorithms and software implementation methods for pattern recognition research. In Proc. IEEE Int. Computer Software Applications Conf., pages 55-63, Chicago, 1979.
12. D. L. Wilson. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics, 2:408-420, 1972.
13. D. Randall Wilson and Tony R. Martinez. Reduction techniques for instance-based learning algorithms. Machine Learning, 38:257-286, 2000.