Texture classification using feature selection and kernel-based techniques-Reference-Cited by-同舟云学术

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Texture classification using feature selection and kernel-based techniques

Published:2015-01-08 Issue:9 Volume:19 Page:2469-2480
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Fernandez-Lozano Carlos,Seoane Jose A.,Gestal Marcos,Gaunt Tom R.,Dorado Julian,Campbell Colin

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-014-1573-5.pdf

Reference105 articles.

1. Abbey CK, Nosratieh A, Sohl-Dickstein J, Yang K, Boone JM (2012) Non-Gaussian statistical properties of breast images. Med Phys 39(11):7121–7130

2. Al-Kadi OS (2010) Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images. Computerized medical imaging and graphics. In: 8th IEEE international conference on bioinformatics and bioengineering, Athens, Greece, 8–10 Oct 2008, vol 34(6, SI), pp 494–503.

3. Alfons A (2012) cvTools: cross-validation tools for regression models. R package version 0.3.2. http://CRAN.R-project.org/package=cvTools

4. Alonso-Atienza F, Rojo-Álvarez JL, Rosado-Muñoz A, Vinagre JJ, García-Alberola A, Camps-Valls G (2012) Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection. Expert Syst Appl 39(2):1956–1967

5. Bac U, Bray M, Caban J, Yao J, Mollura DJ (2012) Computer-assisted detection of infectious lung diseases: a review. Comput Med Imaging Graph 36(1):72–84

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