Huang D, Chow TWS. (2005). Enhancing Density-Based Data Reduction Using Entropy Neural Comput. 18

See more from authors: Huang D · Chow TWS

References and models cited by 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

Blum AL, Langley P. (1997). Selection of relevant feature and examples in machine learning Art Intell. 97

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 Megainduction: Machine learning on very large databases Unpublished doctoral dissertation.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

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

Gray RM. (1984). Vector Quantization IEEE Assp Magazine. 1

Gray RM, Gersho A. (1992). Vector quantization and signal compression.

Han JW, Kamber M. (2001). Data mining: Concepts and techniques.

Hart PE. (1968). The condensed nearest neighbor rule IEEE Trans On Information Theory. 14

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

Kohonen T. (1995). Self-organizing Maps.

Mitra P, Murthy CA, Pal SK. (2002). Density-based multiscale data condensation IEEE Trans On PAMI. 24

Parzen E. (1962). On the estimation of a probability density function and mode Ann Math Stat. 33

Plutowski M, White H. (1993). Selecting concise training sets from clean data. IEEE transactions on neural networks. 4 [PubMed]

Provost F, Kolluri V. (1999). A survey of methods for scaling up inductive algorithms Data Mining And Knowledge Discovery. 2

Quinlan R. (1983). Learning efficient classification procedures and their application to chess end games Machine Learning-an Artificial Intelligence Approach.

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

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

Wu S, Chow TWS. (2004). An online cellular probabilistic self-organizing map for static and dynamic data Sets IEEE Trans On Circuits And Systems. 51

Yang ZP, Zwolinski M. (2001). Mutual information theory for adaptive mixture models IEEE Trans On PAMI. 23

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.