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
 
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DOI
Autor*in
Schneider, Petra; Bunte, Kerstin; Stiekema, Han; Hammer, BarbaraUniBi ; Villmann, Thomas; Biehl, Michael
Einrichtung
Technische Fakultät > AG Machine Learning
Center of Excellence - Cognitive Interaction Technology CITEC
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

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