Batch and Median Neural Gas

Cottrell M, Hammer B, Hasenfuss A, Villmann T (2006)
Neural Networks 19(6-7): 762-771.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
DOI
Autor*in
Cottrell, M.; Hammer, BarbaraUniBi ; Hasenfuss, A.; Villmann, T.
Einrichtung
Technische Fakultät > AG Machine Learning
Erscheinungsjahr
2006
Zeitschriftentitel
Neural Networks
Band
19
Ausgabe
6-7
Seite(n)
762-771
ISSN
0893-6080
Page URI
https://pub.uni-bielefeld.de/record/1993391

Zitieren

Cottrell M, Hammer B, Hasenfuss A, Villmann T. Batch and Median Neural Gas. Neural Networks. 2006;19(6-7):762-771.
Cottrell, M., Hammer, B., Hasenfuss, A., & Villmann, T. (2006). Batch and Median Neural Gas. Neural Networks, 19(6-7), 762-771. https://doi.org/10.1016/j.neunet.2006.05.018
Cottrell, M., Hammer, Barbara, Hasenfuss, A., and Villmann, T. 2006. “Batch and Median Neural Gas”. Neural Networks 19 (6-7): 762-771.
Cottrell, M., Hammer, B., Hasenfuss, A., and Villmann, T. (2006). Batch and Median Neural Gas. Neural Networks 19, 762-771.
Cottrell, M., et al., 2006. Batch and Median Neural Gas. Neural Networks, 19(6-7), p 762-771.
M. Cottrell, et al., “Batch and Median Neural Gas”, Neural Networks, vol. 19, 2006, pp. 762-771.
Cottrell, M., Hammer, B., Hasenfuss, A., Villmann, T.: Batch and Median Neural Gas. Neural Networks. 19, 762-771 (2006).
Cottrell, M., Hammer, Barbara, Hasenfuss, A., and Villmann, T. “Batch and Median Neural Gas”. Neural Networks 19.6-7 (2006): 762-771.

7 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

RNAfitme: a webserver for modeling nucleobase and nucleoside residue conformation in fixed-backbone RNA structures.
Antczak M, Zok T, Osowiecki M, Popenda M, Adamiak RW, Szachniuk M., BMC Bioinformatics 19(1), 2018
PMID: 30134831
Knowledge extraction from a nitrification denitrification wastewater treatment plant using SOM-NG algorithm.
Machón-González I, Rodríguez-Iglesias J, López-García H, Castrillón-Peláez L, Marañón-Maison E., Environ Technol 38(12), 2017
PMID: 27681161
Prototype-based models in machine learning.
Biehl M, Hammer B, Villmann T., Wiley Interdiscip Rev Cogn Sci 7(2), 2016
PMID: 26800334
Comparing feed-forward versus neural gas as estimators: application to coke wastewater treatment.
Machón-González I, López-García H, Rodríguez-Iglesias J, Marañón-Maison E, Castrillón-Peláez L, Fernández-Nava Y., Environ Technol 34(9-12), 2013
PMID: 24191445
Linear time relational prototype based learning.
Gisbrecht A, Mokbel B, Schleif FM, Zhu X, Hammer B., Int J Neural Syst 22(5), 2012
PMID: 22931439
Local matrix learning in clustering and applications for manifold visualization.
Arnonkijpanich B, Hasenfuss A, Hammer B., Neural Netw 23(4), 2010
PMID: 20056379
Topographic mapping of large dissimilarity data sets.
Hammer B, Hasenfuss A., Neural Comput 22(9), 2010
PMID: 20569180

32 References

Daten bereitgestellt von Europe PubMed Central.


AUTHOR UNKNOWN, 0
Laplacian eigenmaps and spectral techniques for embedding and clustering
Belkin, 2002

Blake, 1998

Borg, 1997
Convergence properties of the k-means algorithm
Bottou, 1995
Applications of approximate string matching to 2D shape recognition
Bunke, Pattern Recognition 26(12), 1993
Convergence and ordering of Kohonen’s batch map
Cheng, Neural Computation 9(), 1997

AUTHOR UNKNOWN, 0
Theoretical aspects of the SOM algorithm
Cottrell, Neurocomputing 21(), 1999
SOM-based algorithms for qualitative variables.
Cottrell M, Ibbou S, Letremy P., Neural Netw 17(8-9), 2004
PMID: 15555858

Duda, 2000
Advantages and drawbacks of the Batch Kohonen algorithm
Fort, 2002
A stochastic self-organizing map for proximity data.
Graepel T, Obermayer K., Neural Comput 11(1), 1999
PMID: 9950727
Self-organizing map for clustering in the graph domain
Guenter, Pattern Recognition Letters 23(), 2002
Supervised neural gas with general similarity measure
Hammer, Neural Processing Letters 21(), 2005
Self-organizing maps, vector quantization, and mixture modeling.
Heskes T., IEEE Trans Neural Netw 12(6), 2001
PMID: 18249959
Principle of learning metrics for data analysis
Kaski, In Machine learning for signal processing [Special issue]. Journal of VLSI Signal Processing 37(), 2004

Kohonen, 1995
How to make large self-organizing maps for nonvectorial data.
Kohonen T, Somervuo P., Neural Netw 15(8-9), 2002
PMID: 12416685
Quantitative analysis of 6985 digitized trypsin G-banded human metaphase chromosomes.
Lundsteen C, Philip J, Granum E., Clin. Genet. 18(5), 1980
PMID: 7460372
;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
Topology representing networks
Martinetz, Neural Networks 7(), 1994
Quantifying the local reliability of a sequence alignment.
Mevissen HT, Vingron M., Protein Eng. 9(2), 1996
PMID: 9005433
Data clustering: A review
Murty, ACM Computing Surveys 31(), 1999

AUTHOR UNKNOWN, 0

Ripley, 1996
Online algorithm for the self-organizing map of symbol strings.
Somervuo PJ., Neural Netw 17(8-9), 2004
PMID: 15555863

AUTHOR UNKNOWN, 0
Self-organizing maps and clustering methods for matrix data.
Seo S, Obermayer K., Neural Netw 17(8-9), 2004
PMID: 15555862
Topology preservation in self-organizing feature maps: Exact definition and measurement
Villmann, IEEE Transactions on Neural Networks 2(), 1994

AUTHOR UNKNOWN, 0
A unified framework for model-based clustering
Zhong, Journal of Machine Learning Research 4(), 2003
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 16782307
PubMed | Europe PMC

Suchen in

Google Scholar