Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models-Reference-Cited by-同舟云学术

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Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models

Published:2018-01 Issue:1 Volume:16 Page:117-143
ISSN:1539-2791
Container-title:Neuroinformatics
language:en
Short-container-title:Neuroinform

Author:

Schrouff Jessica,Monteiro J. M.,Portugal L.,Rosa M. J.,Phillips C.,Mourão-Miranda J.

Funder

F.R.S-F.N.R.S Belgian National Research Funds

Fundação para a Ciência e a Tecnologia (PT)

Belgian American Educational Foundation (BE)

Medical Foundation of the Liège Rotary Club

Laboratory of Behavioral and Cognitive Neuroscience at Stanford University

Marie Skłodowska-Curie Actions

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (BR)

Wellcome Trust (GB)

Publisher

Springer Science and Business Media LLC

Subject

Information Systems,General Neuroscience,Software

Link

http://link.springer.com/article/10.1007/s12021-017-9347-8/fulltext.html

Reference82 articles.

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2. Arimura, H., Yoshiura, T., Kumazawa, S., Tanaka, K., Koga, H., Mihara, F., Honda, H., Sakai, S., Toyofuku, F., & Higashida, Y. (2008). Automated method for identification of patients with Alzheimer's disease based on three-dimensional MR images. Academic Radiology, 15(3), 274–284.

3. Bach, F., Lanckriet, G., & Jordan, M. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. Proceedings of the 21st International Conference on Machine Learning (pp. 41–48). Banff: ACM.

4. Baldassarre, L., Mourao-Miranda, J., & Pontil, M. (2012). Structured Sparsity Models for Brain Decoding from fMRI data. Proceedings of the 2nd conference on Pattern Recognition in NeuroImaging. Washington, DC: IEEE Computer Society.

5. Baldassarre, L., Pontil, M., & Mourao-Miranda, J. (2017). Sparsity is better with stability: combining accuracy and stability for model selection in brain decoding. Frontiers in Neuroscience, 11(62). https://doi.org/10.3389/fnins.2017.00062 .

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