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The weights are assigned by a kernel function whose bandwidth is chosen by cross-validation in terms of predictive loglikelihood.
A common problem of these kernel-based methods is to calculate inverses of kernel matrices generated by a kernel function and a set of points.
The dispersal is determined by a kernel function, and the dispersal strategy is defined as the probability of population individuals' moving to a different habitat.
In form, the algorithm minimizes a least-square loss functional adding a coefficient-based ℓ2-penalty term over a linear span of features generated by a kernel function.
This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally.
Unlike ordinary local damping models, the damping force in a non-local model is obtained as a weighted average of the velocity field over the spatial domain, determined by a kernel function based on distance measures.
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The nonlinear regression model is derived by introducing a kernel function into the proposed linear regression model.
The nonlinear implicit mapping provided by the kernel operation allows us to generate an inner product in the feature space by computing a kernel function that has arguments taken from the input space.
By using a kernel function, the embedding in the Hilbert space is actually performed implicitly, that is by specifying the inner product between each pair of points rather than by giving their coordinates explicitly.
Mode calculation is formulated as an iterative scheme of mean calculation, which takes a certain number of feature points and calculates their weighted mean value by using a kernel function, such as the Gaussian.
In nonlinearly separable cases, which almost always occur in classifying compounds of diverse structures [14 17, 50, 61 63], SVM maps the input vectors into a higher dimensional feature space by using a kernel function K(x i, x j ).
More suggestions(16)
by a kernel density
by a membership function
by a gamma function
by a response function
by a step function
by a saturation function
by a propagation function
by a weight function
by a cost function
by a gauge function
by a sign function
by a utility function
by a matrix function
by a kernel mapping
by a deviation function
by a scoring function
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