Sentence examples for multiple kernel algorithm from inspiring English sources

Furthermore, by comparing with the KDA-based method [13], we can also see that introduction of the multiple kernel algorithm into the proposed method is effective.

Since several meteorological elements are related to each other based on atmospheric movements, the proposed method merges such heterogeneous features in the target and neighboring areas based on a multiple kernel algorithm.

Since multiple meteorological elements that have propagated from neighboring areas based on atmospheric movements are related to prediction errors in the target area, we calculated those features from observed wind velocities and merged them by using a multiple kernel algorithm.

Note that since our method adopts a multiple kernel algorithm, we also have to determine γ=[γ0,⋯1,⋯,γ L ]′ in Equation 8. Some methods such as simple MKL [21] have been proposed for determining γ.

We propose here a novel Bayesian multiple kernel learning algorithm for affective classification and retrieval tasks.

In this study, we designed a feature fusion-based localized multiple kernel learning algorithm using the SPM feature to overcome the mentioned difficulties.

In order to effectively make use of these heterogonous theories, we take a kernel-based machine learning paradigm, design and select kernels describing individual similarities according to social network theories, and employ a non-linear multiple kernel learning algorithm to combine the kernels into a unified model.

Multiple kernel learning algorithms combine kernels that naturally correspond to different views, either linearly [53] or nonlinearly [54, 55] to improve learning performance.

Furthermore, MNet constructs a composite network that is coherent to all the labels, whereas most multiple kernel learning algorithms optimize a composite kernel for each binary label, or optimize the composite kernel and the classifier in two separative objectives.

Many researchers have studied multiple kernel learning (MKL) algorithms [19 21].

Multiple Kernel Learning (MKL) algorithms deals with learning the optimal kernel from training data along with learning the function that generates the data.