In this paper, we present a new fast specific complex-valued neural network, the fast Kolmogorov’s Spline Complex Network (FKSCN), which might be advantageous especially in various tasks of pattern recognition. The proposed FKSCN uses cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the nu...

Linear support vector machines (svms) have become popular for solving classification tasks due to their fast and simple online application to large scale data sets. However, many problems are not linearly separable. For these problems kernel-based svms are often used, but unlike their linear variant they suffer from various drawbacks in terms of computational and memory efficiency. Their respon...

As a fundamental technique for graph analysis, graph kernels have been successfully applied to a wide range of problems. Unfortunately, the high computational complexity of existing graph kernels is limiting their further applications to larger-scale graph datasets. In this paper, we propose a fast graph kernel, the descriptor matching (DM) kernel, for graphs with both categorical and numerical...

Examination of the available ignition delay time data and correlations in the case of methane, butane, heptane, decane, kerosene, Jet-A and <span style="font-size: 12pt; color: #000000; font-style: nor...

Sediment transport modeling is of primary importance for the determination channel design velocity in lined channels. This study proposes to model sediment open flow using kernel ridge regression (KRR), a nonlinear technique formulated reproducing Hilbert space. While naïve approach provides high flexibility purposes, regularized variant equipped with an additional mechanism better generalizati...

Kernel Logistic Regression (KLR) is a powerful probabilistic classification tool, but its training and testing both suffer from severe computational bottlenecks when used with large-scale data. Traditionally, L1-penalty is used to induce sparseness in the parameter space for fast testing. However, most of the existing optimization methods for training l1penalized KLR do not scale well in large-...

Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. To this end, explicit feature maps of kernels for vectorial data have been extensively studied. As many real-world data is structured, various kernels for complex data like graphs have been proposed. Indeed, many of them directly compute feat...

When multiplying really large integer operands, the GNU Multiple Precision Arithmetic Library uses a method based on the Fast Fourier Transform. To make an algorithm execute quickly on a modern computer, data has to be available in the cache memory. If that is not the case, a large portion of the execution time will be spent accessing the main memory. It might pay off to perform much extra work...

This paper presents a general high-order kernel regularization technique applicable to all four integral operators of Calderón calculus associated with linear elliptic PDEs in two and three spatial dimensions. Like previous density interpolation methods, the proposed relies on interpolating function around singularity terms solutions underlying homogeneous PDE, so as recast singular nearly inte...