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Pathway aggregation for survival prediction via multiple kernel learning.
Multiple Kernel Learning is used to efficiently combine data from multiple modalities.
And in fact, you can mix this idea with kernel and get multiple kernel learning ideas.
After that, we propose an alternative algorithm with proved convergence to identify the multiple kernel coefficients.
Especially, it outperforms multiple kernel learning in both unsupervised and supervised settings.
We propose here a novel Bayesian multiple kernel learning algorithm for affective classification and retrieval tasks.
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Multiple-kernel learning methods use multiple kernels by combining them into a single one via a combination function.
The multiple-kernel regression, which is a linear combination of basic kernels, is designed to approximate system uncertainties by constructing a multiple-kernel Lagrangian function and computing the corresponding regression parameters.
Maps are computed by means of a multiple-kernel learning machine based on a pre-parcellated atlas (Automatic Anatomic Labeling) (b) Results for the support vector machine classification are shown in the scatter plot.
Multiple-kernel tracking (MKT) can help to solve the target occlusion problem.
For both traits, the multiple-kernel model is substantially more accurate.
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