14.4.4 Self-Organizing Map Classification

Chapter Contents (Back)
Self-Organizing Map.

Lakany, H.M., Schukat-Talamazzini, E.G., Niemann, H., and Ghonaimy, M.A.R.,
Object recognition from 2D images using Kohonen self-organized feature maps,
PRIA(7), 1997, pp. 301-308. BibRef 9700

Hogg, T.[Trevor], Talhami, H.[Habib], Rees, D.[David],
Learning in a self-organising pattern formation system,
PRL(20), No. 1, January 1999, pp. 1-5. BibRef 9901

Suganthan, P.N.,
Pattern classification using multiple hierarchical overlapped self-organising maps,
PR(34), No. 11, November 2001, pp. 2173-2179.
Elsevier DOI 0108
BibRef

Laha, A., Pal, N.R.,
Some novel classifiers designed using prototypes extracted by a new scheme based on self-organizing feature map,
SMC-B(31), No. 6, December 2001, pp. 881-890.
IEEE Top Reference. 0201
See also Design of Vector Quantizer for Image Compression Using Self-Organizing Feature Map and Surface Fitting. BibRef

Wu, S.[Sitao], Chow, T.W.S.[Tommy W. S.],
Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density,
PR(37), No. 2, February 2004, pp. 175-188.
Elsevier DOI 0311
Self organizing map. BibRef

Simon, G., Lendasse, A., Cottrell, M., Fort, J.C., Verleysen, M.,
Time series forecasting: Obtaining long term trends with self-organizing maps,
PRL(26), No. 12, September 2005, pp. 1795-1808.
Elsevier DOI 0508
BibRef

Hagenbuchner, M.[Markus], Tsoi, A.C.[Ah Chung],
A supervised training algorithm for self-organizing maps for structures,
PRL(26), No. 12, September 2005, pp. 1874-1884.
Elsevier DOI 0508
BibRef

Lee, S., Lathrop, R.G.,
Subpixel Analysis of Landsat (rm ETM) Using Self-Organizing Map (SOM) Neural Networks for Urban Land Cover Characterization,
GeoRS(44), No. 6, June 2006, pp. 1642-1654.
IEEE DOI 0606
BibRef

Liu, L.F.[Li-Fan], Wang, B.[Bin], Zhang, L.M.[Li-Ming],
Decomposition of Mixed Pixels Based on Bayesian Self-Organizing Map and Gaussian Mixture Model,
PRL(30), No. 9, 1 July 2009, pp. 820-826.
Elsevier DOI 0905
Decomposition of mixed pixels; Bayesian self-organization map (BSOM); Gaussian mixture model (GMM); Multispectral/hyperspectral data BibRef

Liu, L.[Lifan], Wang, B.[Bin], Zhang, L.M.[Li-Ming],
An approach based on self-organizing map and fuzzy membership for decomposition of mixed pixels in hyperspectral imagery,
PRL(31), No. 11, 1 August 2010, pp. 1388-1395.
Elsevier DOI 1008
Mixed pixel; Spectral unmixing; Endmember; Abundance; Self-organizing map neural network; Fuzzy membership BibRef

Su, M.C.[Mu-Chun], Su, S.Y.[Shi-Yong], Zhao, Y.X.[Yu-Xiang],
A swarm-inspired projection algorithm,
PR(42), No. 11, November 2009, pp. 2764-2786.
Elsevier DOI 0907
Cluster analysis; Projection; Neural networks; Self-organizing feature map; Swarm intelligence BibRef

Sarlin, P.[Peter],
Decomposing the global financial crisis: A Self-Organizing Time Map,
PRL(34), No. 14, 2013, pp. 1701-1709.
Elsevier DOI 1308
Self-Organizing Time Map BibRef

Astudillo, C.A.[César A.], Oommen, B.J.[B. John],
Self-organizing maps whose topologies can be learned with adaptive binary search trees using conditional rotations,
PR(47), No. 1, 2014, pp. 96-113.
Elsevier DOI 1310
Adaptive data structures BibRef

Cabanes, G.[Guénaël], Bennani, Y.[Younès], Destenay, R.[Renaud], Hardy, A.[André],
A new topological clustering algorithm for interval data,
PR(46), No. 11, November 2013, pp. 3030-3039.
Elsevier DOI 1306
Interval data; Clustering; Self-organizing map BibRef

Clark, S.[Stephanie], Sisson, S.A.[Scott. A.], Sharma, A.[Ashish],
A dimension range representation (DRR) measure for self-organizing maps,
PR(53), No. 1, 2016, pp. 276-286.
Elsevier DOI 1602
Self-organizing maps BibRef


O'Connell, C.[Christian], Kutics, A.[Andrea],
Layered Self-Organizing Map for Image Classification in Unrestricted Domains,
CIAP13(I:310-319).
Springer DOI 1311
BibRef

Zheng, H.C.[Hui-Cheng],
Invariant Feature Set Generation with the Linear Manifold Self-organizing Map,
ACCV10(IV: 677-689).
Springer DOI 1011
manifold representation using neural networks to model human brain. BibRef

Yu, D.J.[Dong-Jun], Hancock, E.R.[Edwin R.], Smith, W.A.P.[William A. P.],
A Riemannian Self-Organizing Map,
CIAP09(229-238).
Springer DOI 0909
Generalize SOM to Riemannian space. BibRef

Fornells, A., Golobardes, E., Martorell, J.M., Garrell, J.M., Bernadó, E., Maciá, N.,
Measuring the Applicability of Self-organization Maps in a Case-Based Reasoning System,
IbPRIA07(II: 532-539).
Springer DOI 0706
BibRef

Guan, L.[Ling],
Self-Organizing Trees and Forests: A Powerful Tool in Pattern Clustering and Recognition,
ICIAR06(I: 1-14).
Springer DOI 0610
BibRef

Kyan, M.[Matthew], Guan, L.[Ling],
Local Variance Driven Self-Organization for Unsupervised Clustering,
ICPR06(III: 421-424).
IEEE DOI 0609
BibRef

Smolander, S.[Seppo], and Lampinen, J.[Jouko],
Determining the Optimal Structure for Multilayer Self-Organizing Map with Genetic Algorithm,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Mulier, F., Cherkassky, V.S.[Vladimir S.],
Learning rate schedules for self-organizing maps,
ICPR94(B:224-228).
IEEE DOI 9410
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Context and Structure for Classification .


Last update:Jun 23, 2018 at 14:58:54