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We develop an algorithm to filter them out by combining multiple kernels.
De los Campos et al. [ 14] suggest an interesting venue for further research on the use of KRR for prediction in quantitative genetics, by combining multiple kernels in a single model, each kernel representing a single variance component (e.g., additive, dominance, or epistasis).
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The proposed method adaptively combines multiple kernels, forming a DA classifier that minimizes the bias between the source and target domains.
Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis.
These methods combine multiple kernels (each of which is transformed from a data type) into a single kernel, known as kernel fusion, which is then used for prediction, regression or feature selection.
Designs can be make complex by combining multiple elements together.
Bagging has a powerful classification capacity by combining multiple classifiers.
However, multiclass classification is accomplished by combining multiple binary SVMs.
An ANN is obtained by combining multiple artificial neurons.
The threshold was optimized by combining multiple benchmarks (see below).
If we set δ to 0, the cluster objective is simplified as to averagely combine multiple kernels and Laplacians.
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