Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. Kernel k-Means is a state of the art clustering algorithm. However, in contrast to clustering algorithms that can work using only a limited percentage of the data at a time, Kernel k-M...

> Copper-silica aerogel was synthesized by the sol-gel method and was heated at 400, 500 and 600°C for 3 h in the air. The gained materials were named as sample (a), (b) <span style="font-size: 9pt; colo...

The applicability of non-linear support vector machines (SVMs) has been limited in largescale data collections because of their linear prediction complexity to the size of support vectors. We propose an efficient prediction algorithm with performance guarantee for non-linear SVMs, termed AdaptSVM. It can selectively collapse the kernel function computation to a reduced set of support vectors, c...

"> Accurate prediction of the minimum miscibility pressure (MMP) in a gas injection process is crucial to optimizing the management of gas injection projects. Because the <span style="font-size: 10pt; ...

This paper discusses modiications of the convolution type kernel regression estimator. One modiication uses kernel quantile estimators and is analyzed more detailed. This regression estimator combines advantages of local polynomial and kernel regression estimators and can be applied for small to large sample size. Its properties are illustrated by simulation results and asymptotic theory. Espec...

AI systems typically make decisions and find patterns in data based on the computation of aggregate and specifically sum functions, expressed as queries, on data’s attributes. This computation can become costly or even inefficient when these queries concern the whole or big parts of the data and especially when we are dealing with big data. New types of intelligent analytics require also the ex...