The paper by Duan [27] proposes to implement a multiple kernel learning framework for a transfer learning environment called the domain transfer multiple kernel learning (DTMKL).
We propose here a novel Bayesian multiple kernel learning algorithm for affective classification and retrieval tasks.
To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold.
It is shown that, in a restrictive case (but consistent to prevent overfitting), the problem boils down to a multiple kernel learning instance with infinitely many kernels.
In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP).
In this study, we designed a feature fusion-based localized multiple kernel learning algorithm using the SPM feature to overcome the mentioned difficulties.
Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5].
Multiple kernel learning algorithms combine kernels that naturally correspond to different views, either linearly [53] or nonlinearly [54, 55] to improve learning performance.
M. F. Rogers et al. studied supervised interactive network inference using multiple kernel learning.
In this paper, we have investigated supervised interactive network inference using multiple kernel learning.
Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels.
