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Open AccessJournal ArticleDOI

Tensorial Kernel Based on Spatial Structure Information for Neuroimaging Classification

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TLDR
The method is improved in this manuscript by a new construction of tensorial kernel wherein a 3-order tensor is adopted to preserve the adjacency relation so that calculation of the above huge matrix is avoided, and hence the computational complexity is significantly reduced.
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This article is published in IEICE Transactions on Information and Systems.The article was published on 2017-06-01 and is currently open access. It has received 0 citations till now. The article focuses on the topics: Radial basis function kernel & Multiple kernel learning.

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References
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The Nature of Statistical Learning Theory

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI

Tensor Decompositions and Applications

TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.
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A fast diffeomorphic image registration algorithm

TL;DR: DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.
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Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database

TL;DR: Evaluated the performance of ten high dimensional classification methods proposed to automatically discriminate between patients with Alzheimer's disease or mild cognitive impairment and elderly controls using 509 subjects from the ADNI database, finding whole-brain methods achieved high accuracies and the use of feature selection did not improve the performance but substantially increased the computation times.
Proceedings Article

Multiple Kernel Learning and the SMO Algorithm

TL;DR: It is demonstrated that linear MKL regularised with the p-norm squared, or with certain Bregman divergences, can indeed be trained using SMO, and the resulting algorithm retains both simplicity and efficiency and is significantly faster than state-of-the-art specialised p- norm MKL solvers.
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