References and models cited by this paper | References and models that cite this paper | |
Astrahan MM (1970) Speech analysis by clustering, or the hyperphoneme method Stanford A I Project Memo Bezdek JC, Kuncheva LI (2001) Nearest prototype classifier designs: An experimental study Int J Intell Sys 16:1445-1473 Blum AL, Langley P (1997) Selection of relevant feature and examples in machine learning Art Intell 97:245-271 Catlett J (1991) Megainduction: Machine learning on very large databases Unpublished doctoral dissertation Chang CL (1974) Finding prototypes for nearest neighbor classifiers IEEE Trans Computers 23:1179-1184 Chow TWS, Wu S (2004) An online cellular probabilistic self-organizing map for static and dynamic data Sets IEEE Trans On Circuits And Systems 51:732-747 Cover TM, Thomas JA (1991) Elements of Information Theory Dasarathy BV (1991) Nearest neighbor (NN) norms: NN pattern classification techniques Duda RO, Hart PE, Stork DG (2000) Pattern Classification (2nd edition) Friedman JH (1997) Data mining and statistics: What's the connection? Available online at http:--www.salford-systems.com-doc-dm-stat.pdf Gates GW (1972) The reduced nearest neighbor rule IEEE Trans on Inform Theory 18:431-433 Gersho A, Gray RM (1992) Vector quantization and signal compression Gray RM (1984) Vector Quantization IEEE Assp Magazine 1:4-29 Han JW, Kamber M (2001) Data mining: Concepts and techniques Hart PE (1968) The condensed nearest neighbor rule IEEE Trans On Information Theory 14:515-516 Haykin S (1999) Neural Networks: A Comprehensive Foundation (2nd Ed) Khotanzad A, Lu JH (1990) Classification of invariant image representations using a neural network IEEE Transactions On Signal Process 38:1028-1038 Kohonen T (1995) Self-organizing Maps Mitra P, Murthy CA, Pal SK (2002) Density-based multiscale data condensation IEEE Trans On PAMI 24:734-747 Parzen E (1962) On the estimation of a probability density function and mode Ann Math Stat 33:1064-1076 Plutowski M, White H (1993) Selecting concise training sets from clean data. IEEE Trans Neural Netw 4:305-18 [Journal] [PubMed] Provost F, Kolluri V (1999) A survey of methods for scaling up inductive algorithms Data Mining And Knowledge Discovery 2:131-169 Quinlan R (1983) Learning efficient classification procedures and their application to chess end games Machine Learning-an Artificial Intelligence Approach, Michalski RS:Carbonell JG:Mitechell TM, ed. pp.463 Roy N, Mccallum A (2001) Toward optimal active learning through sampling estimation of error reduction Proc. 18th International Conference on Machine Learning (Available online at www.cs.umass.edu-mccallum-papers-active-icm-101.ps) Schapire RE (1990) The strength of weak learnability Machine Learning 5:197-227 Scott DW (1992) Multivariate density estimation: Theory, practice, and visualization Wilson DR, Martinez TR (2000) Reduction techniques for instance-based learning algorithms Machine Learning 38:257-286 Yang ZP, Zwolinski M (2001) Mutual information theory for adaptive mixture models IEEE Trans On PAMI 23:396-403 |