Sentence examples for the kernel features of from inspiring English sources

Multiple Empirical Kernel Learning (MEKL) explicitly maps samples into different empirical feature spaces in which the kernel features of the mapped samples can be directly provided.

The third step is to design or to generate a new point in the kernel feature space using a kernel feature space algorithm (e.g. the center of highly active compounds).

Most of these approaches try to learn a linear combination of base kernels, which can be interpreted as the concatenation of the base kernel feature space or an 'OR' combination of the individual kernels.

Our approach is explored by the synthesis of three kernel features.

The construction of kernel feature space based on the sparse codes of training samples provides promising implement of curvilinear coordinate system along the flow of nonlinear feature.

We demonstrate the discriminative power of heat kernel features in both synthetic and clinical preterm data.

Our approach avoids a deep redesign and fully benefits from the numerical kernels and features of the original code.

The SKL distance between the kernel density feature vectors of query image, F i, KDE PO, and the training images, F j, KDE PO is then given by Equation 21, in which n is the dimension of the feature vector.

The GWR examines how Nitrogen-footprint influences disease rates within watersheds; comparing the two map series in Fig. 4. The highest-ranked model used an adaptive bandwidth kernel featuring the influence of <15 neighbor watersheds (Table S4).

During construction of the base kernels,, features were grouped into sets.

The construction of a kernel feature space can be concluded as the following steps: (1) Normalize the input data.