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Empirical evaluation of gradient methods for matrix learning vector quantization.

M. LeKander, M. Biehl, and H. de Vries. WSOM, page 197-204. IEEE, (2017)

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Michael Biehl

Hans-Michael Biehl

Helmold Biehl

Günter Biehl

Hugo Biehl

Other publications of authors with the same name

Galaxy classification: A machine learning analysis of GAMA catalogue data.A. Nolte, L. Wang, M. Bilicki, B. Holwerda, and M. Biehl. Neurocomputing, (2019)Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis.E. Mwebaze, and M. Biehl. WSOM, volume 428 of Advances in Intelligent Systems and Computing, page 329-339. Springer, (2016)Insightful stress detection from physiology modalities using Learning Vector Quantization.G. de Vries, S. Pauws, and M. Biehl. Neurocomputing, (2015)Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ.M. Biehl, D. Mudali, K. Leenders, and J. Roerdink. BrainComp, volume 10087 of Lecture Notes in Computer Science, page 131-141. Springer, (2015)Statistical Mechanics of On-Line Learning Under Concept Drift.M. Straat, F. Abadi, C. Göpfert, B. Hammer, and M. Biehl. Entropy, 20 (10): 775 (2018)Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition.G. de Vries, and M. Biehl. Similarity-based learning on structures, volume 09081 of Dagstuhl Seminar Proceedings, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany, (2009)Some steps towards a general principle for dimensionality reduction mappings.B. Hammer, K. Bunte, and M. Biehl. Learning paradigms in dynamic environments, volume 10302 of Dagstuhl Seminar Proceedings, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany, (2010)Relevance matrices in LVQ.P. Schneider, M. Biehl, and B. Hammer. ESANN, page 37-42. (2007)Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders.S. Ghosh, E. Baranowski, R. van Veen, G. de Vries, M. Biehl, W. Arlt, P. Tiño, and K. Bunte. ESANN, (2017)The dynamics of Learning Vector Quantization.M. Biehl, A. Ghosh, and B. Hammer. ESANN, page 13-18. (2005)

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Disambiguation of "Biehl, Michael"

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Empirical evaluation of gradient methods for matrix learning vector quantization.

Please choose a person to relate this publication to

Michael Biehl

Hans-Michael Biehl

Helmold Biehl

Günter Biehl

Hugo Biehl

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Biehl, Michael"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Empirical evaluation of gradient methods for matrix learning vector quantization.

Please choose a person to relate this publication to

Michael Biehl

Hans-Michael Biehl

Helmold Biehl

Günter Biehl

Hugo Biehl

Other publications of authors with the same name

Disambiguation

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URL
DOI
BibTeX
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APA
Chicago
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Empirical evaluation of gradient methods for matrix learning vector quantization.