The minimum regularized covariance determinant method (MRCD) is a robust estimator for multivariate location and scatter, which detects outliers by fitting matrix to the data. Its regularization ensures that well-conditioned in any dimension. MRCD assumes non-outlying observations are roughly elliptically distributed, but many datasets not of form. Moreover, computation time increases substanti...

In this research, the effect ofmixed salts together with mixed ionic surfactants on dropinterface coalescence time was studied for the system of water (d) / toluene(c) as a model system. Sodium dodecyl sulf...

A high performance activated carbon was synthesized using walnut shell as a solid waste through a two-step zinc chloride chemical activation-thermal <span style="font-size: 11pt; color: #000000; font-s...

Random walk graph kernel has been used as an important tool for various data mining tasks including classification and similarity computation. Despite its usefulness, however, it suffers from the expensive computational cost which is at least O(n) or O(m) for graphs with n nodes and m edges. In this paper, we propose Ark, a set of fast algorithms for random walk graph kernel computation. Ark is...

A modified active subset selection method based on quadratic Rényi entropy and a fast cross-validation for fixed-size least-squares support vector machines is proposed for classification and regression with optimized tuning process. The kernel bandwidth of the entropy based selection criterion is optimally determined according to the solve-the-equation plug-in method. Also a fast cross-validati...

&lt;p style='text-indent:20px;'&gt;Kernel matrices are crucial in many learning tasks such as support vector machines or kernel ridge regression. The matrix is typically dense and large-scale. Depending on the dimension of feature space even computation all its entries reasonable time becomes a challenging task. For cost matrix-vector product scales quadratically with dimensionality &lt;inline-...

This paper reports the result of CFD simulation of catalytic oxidation of benzene on monolithic catalyst. The geometries ofthe catalyst and reactor were designed in Gambit <span style="font-size: 10pt;...

This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead Gaussian kernel. outperforms in two aspects. First, use t-SNE overcomes drawback misrepresenting some structures data, which often occurs when is applied t-SNE. because determines each local bandwidth based on one point only, while derived directly from da...