Support Vector Machines, SVM, Surveys, Reviews, General

Chapter Contents (Back)
Support Vector Machines. SVM. Survey, SVM.

Cortes, C.[Corinna], Vapnik, V.[Vladimir],
Support-Vector Networks,
MachLearn(20), No. 3, 1995, pp. 273-297. Initial description for SVM ideas. 0906

Vapnik, V.[Vladimir],
The Support Vector Method,
ICANN97(263-271). 0906

Schölkopf, B.[Bernhard], Burges, C.[Chris], Vapnik, V.[Vladimir],
Incorporating Invariances in Support Vector Learning Machines,
ICANN96(47-52). 0906

Chang, C.C., Lin, C.J.,
LIBSVM: a library for support vector machines,
WWW Link. Code, Support Vector Machines. BibRef 0100

LIBSVMTL: a Support Vector Machine Template Library,
HTML Version. Code, Support Vector Machines. Based on LIBSVM above. BibRef 0100

Schölkopf, B.[Bernhard],
Support Vector Machines,
Oldenbourg Verlag: Munich, 1997. BibRef 9700

Schölkopf, B.[Bernhard],
Support Vector Learning,
R. Oldenbourg VerlagMunich, 1997.
WWW Link. BibRef 9700

Scholkopf, B.[Bernhard], Smola, A.J.[Alexander J.], Muller, K.R.[Klaus-Robert], Bartlett, P.L.,
New Support Vector Algorithms,
NeurComp(12), 2000, pp. 1207-1245. BibRef 0001

Cristianini, N.[Nello], Schölkopf, B.[Bernhard],
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines,
AIMag(23), No. 3, Fall 2002, pp. 31-41. Survey, SVM. Survey and general discussion. BibRef 0200

Kienzle, W.[Wolf], Bakir, G.H.[Gökhan H.], Franz, M.O.[Matthias O.], Schölkopf, B.[Bernhard],
Efficient Approximations for Support Vector Machines in Object Detection,
Springer DOI 0505

Cristianini, N.[Nello], Shawe-Taylor, J.[John],
An Introduction to Support Vector Machines,
Cambridge University Press2000. Survey, SVM.
WWW Link. ISBN: 0 521 78019 5 Buy this book: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods BibRef 0001

Chapelle, O., Haffner, P., Vapnik, V.,
Support Vector Machines for Histogram-Based Image Classification,
TNN(10), No. 5, May 1999, pp. 1055-1064. BibRef 9905

Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Toga, A.W., Thompson, P.M.,
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation,
MedImg(29), No. 1, January 2010, pp. 30-43.

Abe, S.[Shigeo],
Support Vector Machines for Pattern Classification,
Springer-Verlag2010. ISBN: 978-1-84996-097-7
WWW Link. Survey, Support Vector Machines. Buy this book: Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) Overview and analysis of SVM techniques. Design, training. BibRef 1000

Mountrakis, G.[Giorgos], Im, J.[Jungho], Ogole, C.[Caesar],
Support vector machines in remote sensing: A review,
PandRS(66), No. 3, May 2011, pp. 247-259.
Elsevier DOI 1103
Survey, Support Vector Machines. Support vector machines; Review; Remote sensing; SVM; SVMs BibRef

Heydari, S.S.[Shahriar S.], Mountrakis, G.[Giorgos],
Meta-analysis of deep neural networks in remote sensing: A comparative study of mono-temporal classification to support vector machines,
PandRS(152), 2019, pp. 192-210.
Elsevier DOI 1905
Deep learning, Classification, Convolutional neural network, Deep belief network, Stacked auto encoder, Support vector machine BibRef

Löw, F., Michel, U., Dech, S., Conrad, C.,
Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines,
PandRS(85), No. 1, 2013, pp. 102-119.
Elsevier DOI 1310
Crop classification BibRef

Löw, F.[Fabian], Knöfel, P.[Patrick], Conrad, C.[Christopher],
Analysis of uncertainty in multi-temporal object-based classification,
PandRS(105), No. 1, 2015, pp. 91-106.
Elsevier DOI 1506
Classification uncertainty BibRef

Löw, F.[Fabian], Duveiller, G.[Grégory], Conrad, C.[Christopher], Michel, U.[Ulrich],
Impact of Categorical and Spatial Scale on Supervised Crop Classification using Remote Sensing,
PFG(2015), No. 1, 2015, pp. 7-20.
DOI Link 1503

Yang, B.[Bo], Shao, Q.M.[Quan-Ming], Pan, L.[Li], Li, W.B.[Wen-Bin],
A study on regularized Weighted Least Square Support Vector Classifier,
PRL(108), 2018, pp. 48-55.
Elsevier DOI 1805
Weighted Least Square Support Vector Classifier, Regularization technique, Robust estimation, Sparse classifier BibRef

Zhu, X.F.[Xiu-Fang], Li, N.[Nan], Pan, Y.Z.[Yao-Zhong],
Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

Arvanitopoulos, N.[Nikolaos], Bouzas, D.[Dimitrios], Tefas, A.[Anastasios],
Laplacian Support Vector Analysis for Subspace Discriminative Learning,
Algorithm design and analysis BibRef

Orfanidis, G.[Georgios], Tefas, A.[Anastasios],
Exploiting subclass information in Support Vector Machines,
WWW Link. 1302

Gavriilidis, V.[Vasileios], Tefas, A.[Anastasios],
Random Walk Kernel Applications to Classification Using Support Vector Machines,
Covariance matrices BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Extreme Learning Machine, ELM .

Last update:Jul 18, 2024 at 20:50:34