Regularization in Matrix Relevance Learning

Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M (2010)
IEEE Transactions on Neural Networks 21(5): 831-840.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
DOI
Autor*in
Schneider, Petra; Bunte, Kerstin; Stiekema, Han; Hammer, BarbaraUniBi ; Villmann, Thomas; Biehl, Michael
Einrichtung
Center of Excellence - Cognitive Interaction Technology CITEC
Technische Fakultät > AG Machine Learning
Stichworte
metric adaptation; regularization; Cost function; learning vector quantization (LVQ)
Erscheinungsjahr
2010
Zeitschriftentitel
IEEE Transactions on Neural Networks
Band
21
Ausgabe
5
Seite(n)
831-840
ISSN
1045-9227
eISSN
1941-0093
Page URI
https://pub.uni-bielefeld.de/record/1795962

Zitieren

Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M. Regularization in Matrix Relevance Learning. IEEE Transactions on Neural Networks. 2010;21(5):831-840.
Schneider, P., Bunte, K., Stiekema, H., Hammer, B., Villmann, T., & Biehl, M. (2010). Regularization in Matrix Relevance Learning. IEEE Transactions on Neural Networks, 21(5), 831-840. https://doi.org/10.1109/TNN.2010.2042729
Schneider, Petra, Bunte, Kerstin, Stiekema, Han, Hammer, Barbara, Villmann, Thomas, and Biehl, Michael. 2010. “Regularization in Matrix Relevance Learning”. IEEE Transactions on Neural Networks 21 (5): 831-840.
Schneider, P., Bunte, K., Stiekema, H., Hammer, B., Villmann, T., and Biehl, M. (2010). Regularization in Matrix Relevance Learning. IEEE Transactions on Neural Networks 21, 831-840.
Schneider, P., et al., 2010. Regularization in Matrix Relevance Learning. IEEE Transactions on Neural Networks, 21(5), p 831-840.
P. Schneider, et al., “Regularization in Matrix Relevance Learning”, IEEE Transactions on Neural Networks, vol. 21, 2010, pp. 831-840.
Schneider, P., Bunte, K., Stiekema, H., Hammer, B., Villmann, T., Biehl, M.: Regularization in Matrix Relevance Learning. IEEE Transactions on Neural Networks. 21, 831-840 (2010).
Schneider, Petra, Bunte, Kerstin, Stiekema, Han, Hammer, Barbara, Villmann, Thomas, and Biehl, Michael. “Regularization in Matrix Relevance Learning”. IEEE Transactions on Neural Networks 21.5 (2010): 831-840.

7 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Feature Selection With $\ell_{2,1-2}$ Regularization.
Yong Shi, Jianyu Miao, Zhengyu Wang, Peng Zhang, Lingfeng Niu., IEEE Trans Neural Netw Learn Syst 29(10), 2018
PMID: 29994757
Prototype-based models in machine learning.
Biehl M, Hammer B, Villmann T., Wiley Interdiscip Rev Cogn Sci 7(2), 2016
PMID: 26800334
Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.
Boareto M, Cesar J, Leite VB, Caticha N., IEEE/ACM Trans Comput Biol Bioinform 12(3), 2015
PMID: 26357281
Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface.
Zhang Y, Zhou G, Jin J, Zhao Q, Wang X, Cichocki A., Int J Neural Syst 24(1), 2014
PMID: 24344691
Analysis of flow cytometry data by matrix relevance learning vector quantization.
Biehl M, Bunte K, Schneider P., PLoS One 8(3), 2013
PMID: 23527184
Texture feature ranking with relevance learning to classify interstitial lung disease patterns.
Huber MB, Bunte K, Nagarajan MB, Biehl M, Ray LA, Wismüller A., Artif Intell Med 56(2), 2012
PMID: 23010586
Discriminative least squares regression for multiclass classification and feature selection.
Xiang S, Nie F, Meng G, Pan C, Zhang C., IEEE Trans Neural Netw Learn Syst 23(11), 2012
PMID: 24808069

20 References

Daten bereitgestellt von Europe PubMed Central.

generalized relevance learning vector quantization for classification driven feature extraction from hyperspectral data
mendenhall, Proc ASPRS Annu Conf Technol Exhib (), 2006

AUTHOR UNKNOWN, 0
Distance learning in discriminative vector quantization.
Schneider P, Biehl M, Hammer B., Neural Comput 21(10), 2009
PMID: 19635012

biehl, Stationarity of relevance matrix learning vector quantization (), 2009
Soft learning vector quantization.
Seo S, Obermayer K., Neural Comput 15(7), 2003
PMID: 12816567

bunte, Limited rank matrix learning and discriminative visualization (), 2008

AUTHOR UNKNOWN, 0

petersen, 2008

newman, UCI repository of machine learning databases (), 1998
learning the kernel with hyperkernels
ong, J Mach Learn Res 6(), 2005
Soft nearest prototype classification.
Seo S, Bode M, Obermayer K., IEEE Trans Neural Netw 14(2), 2003
PMID: 18238021

sato, Advances in Neural Information Processing Systems 8 (), 1996
Adaptive relevance matrices in learning vector quantization.
Schneider P, Biehl M, Hammer B., Neural Comput 21(12), 2009
PMID: 19764875
relevance determination in learning vector quantization
bojer, Proc Eur Symp Artif Neural Netw (), 2001

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, Bibliography on the self-organizing map (SOM) and learning vector quantization (LVQ) (), 2002

AUTHOR UNKNOWN, 0

kohonen, Self-Organizing Maps (), 1997
Generalized relevance learning vector quantization.
Hammer B, Villmann T., Neural Netw 15(8-9), 2002
PMID: 12416694

AUTHOR UNKNOWN, 0
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 20236882
PubMed | Europe PMC

Suchen in

Google Scholar