Lesaja et al. [10] also proposed IPMs for P ∗ -LCPs based on ten kernel functions which were defined for LO problems.
$$\end{document} In our study, accuracy of genomic prediction based on multi-line training was evaluated for two non-linear models that were based on two different kernels that are the most representative of the two categories of kernels described in the previous section [ 35].
We propose and analyze a novel framework for learning sparse representations based on two statistical techniques: kernel smoothing and marginal regression.
Bai-Ghami-Roos [5] proposed a new primal-dual IPM for linear optimization (LO) problem based on eligible barrier functions and a unified scheme for analyzing the algorithm based on four conditions of the kernel function and Bai-Lesaja-Roos [6] generalized to P ∗ -LCP.
We define the weight kernel W based on three length functions of the path: its length l s (P) in the image, its length l d (P) in the disparity label space, and a length δ L (discussed below) that takes into account potential disparity discontinuities along the path.
The morphological evaluation of these materials was based on eight morphological traits of the mature spikes (kernel-row number, spike density, lemma awn barbs, glume colour, lemma type, length of rachilla hair, kernel cover, and lemma colour), as reported by the Bioversity International Barley descriptors [ 51]; the details of the sampling strategy and morphological evaluation are given in [ 16].
The kernel represents the target pair using graph matrices based on two subgraphs, where the graph features include all nonzero elements in the graph matrices.
We based the simulator on six kernels: the multibody system dynamics kernel, the force calculation kernel, the numerical analysis kernel, the hybrid simulation kernel, the scenario management kernel, and the collision detection kernel.
For our analysis, we used kernels from six heterogeneous data sets that have been used for supervised interactive network inference in a previous study [ 1]: three based on protein sequence kernels and three based on diffusion kernels.
For each dataset we built three SVM models, implemented with the LibSVM package (Chang and Lin, 2001), that are based on the three available kernels including the radial basis function (RBF), polynomial (POLY), which also includes the linear kernel, and sigmoid (SIG).
