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It is a very important step in fitting kernel models to maximize generalization performance.
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The results show that the proposed algorithm is capable of (1) modeling the uncertainties between data sampling points of time series; (2) maximizing generalization performance; (3) and finally costing lower computational cost.
An SVR model developed by using MATLAB® was optimized using a GA, maximizing generalization performance.
An SVR model was then optimized through SAFCA to maximize its generalization performance.
The random distance between two signatures calculated for a pair of genomes from a group is given by the following equation: (7) where M is a diagonal matrix learned by maximizing estimated generalization performance using randomly selected training data.
The group-specific distance between two signatures of genomes from a group is given by the following: (6) where M is a diagonal matrix learned by maximizing the estimated generalization performance with training data from the same group (as x and y).
Therefore, our experimental setup consisted of randomly sampling genomes of 30 species (one genome per species) from a group and then learning a Mahalanobis metric from the corresponding l4n1 signatures, guided by the target phenetic distances, such that the estimated generalization performance is maximized (see Materials and Methods).
Concretely, considering its similar principle to the support vector machines of maximizing the separation margin and superior generalization performance, the modified HK algorithm (MHKS) is chosen and then a matrix-based MHKS classifier (MatMHKS) is developed.
3) ELM has good generalization performance.
Fair estimation of the generalization performance.
In this section, we evaluate the generalization performance of BFSD.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com