Comparison of relevance learning vector quantization with other metric adaptive classification methods

Villmann T, Schleif F-M, Hammer B (2006)
Neural Networks 19(5): 610-622.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
DOI
Autor*in
Villmann, T.; Schleif, Frank-MichaelUniBi ; Hammer, BarbaraUniBi
Einrichtung
Technische Fakultät > AG Machine Learning
Erscheinungsjahr
2006
Zeitschriftentitel
Neural Networks
Band
19
Ausgabe
5
Seite(n)
610-622
ISSN
0893-6080
Page URI
https://pub.uni-bielefeld.de/record/1994237

Zitieren

Villmann T, Schleif F-M, Hammer B. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks. 2006;19(5):610-622.
Villmann, T., Schleif, F. - M., & Hammer, B. (2006). Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks, 19(5), 610-622. https://doi.org/10.1016/j.neunet.2005.07.013
Villmann, T., Schleif, Frank-Michael, and Hammer, Barbara. 2006. “Comparison of relevance learning vector quantization with other metric adaptive classification methods”. Neural Networks 19 (5): 610-622.
Villmann, T., Schleif, F. - M., and Hammer, B. (2006). Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks 19, 610-622.
Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks, 19(5), p 610-622.
T. Villmann, F.-M. Schleif, and B. Hammer, “Comparison of relevance learning vector quantization with other metric adaptive classification methods”, Neural Networks, vol. 19, 2006, pp. 610-622.
Villmann, T., Schleif, F.-M., Hammer, B.: Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks. 19, 610-622 (2006).
Villmann, T., Schleif, Frank-Michael, and Hammer, Barbara. “Comparison of relevance learning vector quantization with other metric adaptive classification methods”. Neural Networks 19.5 (2006): 610-622.

45 References

Daten bereitgestellt von Europe PubMed Central.

An information energy LVQ approach for feature ranking
Andonie, 2004

Blake, 1998

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Text classification using string kernels
Cristianini, Journal of Machine Learning Research 2(), 2002
A discriminative framework for detecting remote protein homologies
Diekhans, Journal of Computational Biology 7(1–2), 2000

Fano, 1961

AUTHOR UNKNOWN, 0
A survey of kernels for structured data
Gärtner, 2003
Processing directed acyclic graphs with recursive neural networks.
Bianchini M, Gori M, Scarselli F., IEEE Trans Neural Netw 12(6), 2001
PMID: 18249974
A general framework for adaptive processing of data structures.
Frasconi P, Gori M, Sperduti A., IEEE Trans Neural Netw 9(5), 1998
PMID: 18255765
Neural methods for non-standard data
Hammer, 2004
Supervised neural gas with general similarity measure
Hammer, Neural Processing Letters 21(1), 2005
On the generalization ability of GRLVQ networks
Hammer, Neural Processing Letters 21(2), 2005
Prototype based recognition of splice sites
Hammer, 2005
Generalized relevance learning vector quantization.
Hammer B, Villmann T., Neural Netw 15(8-9), 2002
PMID: 12416694
Mathematical aspects of neural networks
Hammer, 2003
Probability of error, equivocation and the chernoff bound
Hellmann, IEEE Transactions on Information Theory 16(), 1970

Kapur, 1994

Kapur, 1992

AUTHOR UNKNOWN, 0

Kohonen, 1995
How to make large self-organizing maps for nonvectorial data.
Kohonen T, Somervuo P., Neural Netw 15(8-9), 2002
PMID: 12416685
;Neural-gas' network for vector quantization and its application to time-series prediction.
Martinetz TM, Berkovich SG, Schulten KJ., IEEE Trans Neural Netw 4(4), 1993
PMID: 18267757

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Theorie de l'information energie informationelle
Onicescu, Comptes rendus de l'Academie des Sciences Series A–B, Tome 263(), 1966
Predicting the disulfide bonding state of cysteines with combinations of kernel machines
Passerini, Journal of VLSI Signal Processing 35(3), 2003

Press, 1999
Information theoretic learning
Principe, 2000

AUTHOR UNKNOWN, 0
A formulation of learning vector quantization using a new misclassification measure
Sato, 1998
Generalized learning vector quantization
Sato, 1995
An analysis of convergence in generalized LVQ
Sato, 1998

AUTHOR UNKNOWN, 0

Schölkopf, 2002
Contextual processing of structured data by recursive cascade correlation.
Micheli A, Sona D, Sperduti A., IEEE Trans Neural Netw 15(6), 2004
PMID: 15565768
Supervised neural networks for the classification of structures.
Sperduti A, Starita A., IEEE Trans Neural Netw 8(3), 1997
PMID: 18255672
Feature extraction by non-parametric mutual information maximization
Torkkola, Journal of Machine Learning Research 3(), 2003

AUTHOR UNKNOWN, 0
Distance metric learning with kernels
Tsang, 2003
Supervised neural gas for learning vector quantization
Villmann, 2002
Parametric distance metric learning with label information
Zhang, 2003

AUTHOR UNKNOWN, 0
Applying classification separability analysis to microarray data
Zhang, 2002
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 16343848
PubMed | Europe PMC

Suchen in

Google Scholar