Prototype-based models in machine learning

Biehl M, Hammer B, Villmann T (2016)
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
DOI
Autor*in
Biehl, Michael; Hammer, BarbaraUniBi ; Villmann, Thomas
Einrichtung
Center of Excellence - Cognitive Interaction Technology CITEC
Technische Fakultät > AG Machine Learning
Erscheinungsjahr
2016
Zeitschriftentitel
Wiley Interdisciplinary Reviews: Cognitive Science
Band
7
Ausgabe
2
Seite(n)
92-111
ISSN
1939-5078
Page URI
https://pub.uni-bielefeld.de/record/2910957

Zitieren

Biehl M, Hammer B, Villmann T. Prototype-based models in machine learning. Wiley Interdisciplinary Reviews: Cognitive Science. 2016;7(2):92-111.
Biehl, M., Hammer, B., & Villmann, T. (2016). Prototype-based models in machine learning. Wiley Interdisciplinary Reviews: Cognitive Science, 7(2), 92-111. doi:10.1002/wcs.1378
Biehl, Michael, Hammer, Barbara, and Villmann, Thomas. 2016. “Prototype-based models in machine learning”. Wiley Interdisciplinary Reviews: Cognitive Science 7 (2): 92-111.
Biehl, M., Hammer, B., and Villmann, T. (2016). Prototype-based models in machine learning. Wiley Interdisciplinary Reviews: Cognitive Science 7, 92-111.
Biehl, M., Hammer, B., & Villmann, T., 2016. Prototype-based models in machine learning. Wiley Interdisciplinary Reviews: Cognitive Science, 7(2), p 92-111.
M. Biehl, B. Hammer, and T. Villmann, “Prototype-based models in machine learning”, Wiley Interdisciplinary Reviews: Cognitive Science, vol. 7, 2016, pp. 92-111.
Biehl, M., Hammer, B., Villmann, T.: Prototype-based models in machine learning. Wiley Interdisciplinary Reviews: Cognitive Science. 7, 92-111 (2016).
Biehl, Michael, Hammer, Barbara, and Villmann, Thomas. “Prototype-based models in machine learning”. Wiley Interdisciplinary Reviews: Cognitive Science 7.2 (2016): 92-111.

Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

92 References

Daten bereitgestellt von Europe PubMed Central.

Generalization and similarity in exemplar models of categorization: insights from machine learning.
Jakel F, Scholkopf B, Wichmann FA., Psychon Bull Rev 15(2), 2008
PMID: 18488638

Kohonen, 1997

Bishop, 2007

Hastie, 2009
;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

Duda, 2001
Least square quantization in PCM
Lloyd, IEEE Trans Inf Theory 28(), 1982

Ritter, 1992
A stochastic approximation method
Robbins, Ann Math Stat 22(), 1951

Sra, 2011
Automatic segmentation of lung nodules with growing neural gas and support vector machine.
Magalhaes Barros Netto S, Correa Silva A, Acatauassu Nunes R, Gattass M., Comput. Biol. Med. 42(11), 2012
PMID: 23021776
Online data visualization using the neural gas network.
Estevez PA, Figueroa CJ., Neural Netw 19(6-7), 2006
PMID: 16806817
Sparse coding neural gas for the separation of noisy overcomplete sources
Labusch, 2008

Qin, 2004

Strickert, 2003

Walter, 1991

Kohonen, 2014

AUTHOR UNKNOWN, 0

Heskes, 1999
Topology preservation in self-organizing feature maps: exact definition and measurement.
Villmann T, Der R, Herrmann M, Martinetz TM., IEEE Trans Neural Netw 8(2), 1997
PMID: 18255630
Quantifying the neighborhood preservation of self-organizing feature maps.
Bauer HU, Pawelzik KR., IEEE Trans Neural Netw 3(4), 1992
PMID: 18276457
SOM-based data visualization methods
Vesanto, Intell Data Anal 3(), 1999
Self-organizing map for data mining in MATLAB: the SOM toolbox
Vesanto, Simul News Europe 25(), 1999
The use of multiple measurements in taxonomic problems
Fisher, Ann Eugenics 7(), 1936

AUTHOR UNKNOWN, 0

Ultsch, 1990
Batch and median neural gas.
Cottrell M, Hammer B, Hasenfuss A, Villmann T., Neural Netw 19(6-7), 2006
PMID: 16782307
The self-organizing map, the Geo-SOM, and relevant variants for geosciences
Bação, Comput Geosci 31(), 2005
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors.
Laaksonen J, Koskela M, Oja E., IEEE Trans Neural Netw 13(4), 2002
PMID: 18244480
Websom for textual data mining
Lagus, Artif Intell Rev 13(), 1999
Classification of hyperspectral imagery with neural networks: comparison to conventional tools
Merényi, EURASIP J Adv Signal Proc 71(), 2014
Self-organizing feature maps for modeling and control of robotic manipulators
Barreto, J Intell Robot Syst 36(), 2003
Anomaly detection in mobile communication networks using the self-organizing map
Frota, J Intell Fuzzy Syst 18(), 2007
Regional models: a new approach for nonlinear system identification via clustering of the self-organizing map
Souza, Neurocomputing 147(), 2015
Forecasting the CATS benchmark with the double vector quantization method
Simon, Neurocomputing 70(), 2007
SOM's mathematics.
Fort JC., Neural Netw 19(6-7), 2006
PMID: 16822649
GTM: the generative topographic mapping
Bishop, Neural Comput 10(), 1998
Visualization of tree-structured data through generative topographic mapping.
Gianniotis N, Tino P., IEEE Trans Neural Netw 19(8), 2008
PMID: 18701375
Hierarchical GTM: constructing localized nonlinear projection manifolds in a principled way
Tiño, IEEE Trans Pattern Anal Mach Intell 24(), 2002
Data visualization by nonlinear dimensionality reduction
Gisbrecht, WIREs Data Min Knowl Discov 5(), 2015
A taxonomy for spatiotemporal connectionist networks revisited: the unsupervised case.
Barreto Gde A, Araujo AF, Kremer SC., Neural Comput 15(6), 2003
PMID: 12816574
A general framework for unsupervised processing of structured data
Hammer, Neurocomputing 57(), 2004
Recursive self-organizing network models.
Hammer B, Micheli A, Sperduti A, Strickert M., Neural Netw 17(8-9), 2004
PMID: 15555852
Batch kernel SOM and related laplacian methods for social network analysis
Boulet, Neurocomputing 71(), 2008
Topographic mapping of large dissimilarity data sets.
Hammer B, Hasenfuss A., Neural Comput 22(9), 2010
PMID: 20569180
On the equivalence between kernel self-organising maps and self-organising mixture density networks.
Yin H., Neural Netw 19(6-7), 2006
PMID: 16759835
How to make large self-organizing maps for nonvectorial data.
Kohonen T, Somervuo P., Neural Netw 15(8-9), 2002
PMID: 12416685

Kohonen, 1990
A review of learning vector quantization classifiers
Nova, Neural Comput Appl 25(), 2014
Nearest neighbor pattern classification
Cover, IEEE Trans Inf Theory 13(), 1967

Hagenbuchner, 2001
Supervised neural gas with general similarity measure
Hammer, Neural Process Lett 21(), 2005
The condensed nearest neighbor rule
Hart, IEEE Trans Inf Theory 14(), 1968
Improved k-nearest neighbor classification
Wu, Pattern Recogn 35(), 2002
Dynamics and generalization ability of LVQ algorithms
Biehl, J Mach Learn Res 8(), 2007

Sato, 1996

Sato, 1998
Soft nearest prototype classification.
Seo S, Bode M, Obermayer K., IEEE Trans Neural Netw 14(2), 2003
PMID: 18238021
Soft learning vector quantization.
Seo S, Obermayer K., Neural Comput 15(7), 2003
PMID: 12816567

Schölkopf, 2002

Shawe-Taylor, 2004

Biehl, 2014

Hammer, 2005

Biehl, 2007
Distance-based classification of handwritten symbols
Golubitsky, Int J Doc Anal Recognit 13(), 2010
On the generalised distance in statistics
Mahalanobis, Proc Natl Instit Sci India 2(), 1936
The kernel trick for distances
Schölkopf, 2001

Villmann, 2012

Cichocki, 2009
Divergence based classification and learning vector quantization
Mwebaze, Neurocomputing 74(), 2011
Clustering with Bregman divergences
Banerjee, J Mach Learn Res 6(), 2005

Jang, 2008

Inokuchi, 2004

Lee, 2005

AUTHOR UNKNOWN, 0
Limited Rank Matrix Learning, discriminative dimension reduction and visualization.
Bunte K, Schneider P, Hammer B, Schleif FM, Villmann T, Biehl M., Neural Netw 26(), 2011
PMID: 22041220
Adaptive relevance matrices in learning vector quantization.
Schneider P, Biehl M, Hammer B., Neural Comput 21(12), 2009
PMID: 19764875
Regularization in matrix relevance learning.
Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M., IEEE Trans Neural Netw 21(5), 2010
PMID: 20236882
Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors.
Arlt W, Biehl M, Taylor AE, Hahner S, Libe R, Hughes BA, Schneider P, Smith DJ, Stiekema H, Krone N, Porfiri E, Opocher G, Bertherat J, Mantero F, Allolio B, Terzolo M, Nightingale P, Shackleton CH, Bertagna X, Fassnacht M, Stewart PM., J. Clin. Endocrinol. Metab. 96(12), 2011
PMID: 21917861
Analysis of flow cytometry data by matrix relevance learning vector quantization.
Biehl M, Bunte K, Schneider P., PLoS ONE 8(3), 2013
PMID: 23527184
Learning effective color features for content based image retrieval in dermatology
Bunte, Pattern Recogn 44(), 2011
Online figure-ground segmentation with adaptive metrics in generalized LVQ
Denecke, Neurocomputing 72(), 2009

AUTHOR UNKNOWN, 0
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

Weinberger, 2006
Distance metric learning for large margin nearest neighbor classification
Weinberger, J Mach Learn Res 10(), 2009
Distance learning in discriminative vector quantization.
Schneider P, Biehl M, Hammer B., Neural Comput 21(10), 2009
PMID: 19635012

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Learning vector quantization for (dis-)similarities
Hammer, Neurocomputing 131(), 2014

Pevsner, 2003
Metric learning for sequences in relational LVQ
Mokbel, Neurocomputing 169(), 2015
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 26800334
PubMed | Europe PMC

Suchen in

Google Scholar