We review machine learning methods employing positive definite kernels. These methods
formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of …

The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi
hotspots, and distributed networks like Tor has drastically complicated the task of identifying …

Spatial transcriptomic studies are becoming increasingly common and large, posing
important statistical and computational challenges for many analytic tasks. Here, we present …

Kernel classifiers and regressors designed for structured data, such as sequences, trees
and graphs, have significantly advanced a number of interdisciplinary areas such as …

This paper studies the problem of automatic detection of false rumors on Sina Weibo, the
popular Chinese microblogging social network. Traditional feature-based approaches …

We give a basic introduction to Gaussian Process regression models. We focus on
understanding the role of the stochastic process and how it is used to define a distribution …

Pattern Analysis is the process of finding general relations in a set of data, and forms the
core of many disciplines, from neural networks, to so-called syntactical pattern recognition …

This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …

We describe the maximum-likelihood parameter estimation problem and how the
Expectation-Maximization (EM) algorithm can be used for its solution. We first describe the …

Kernel-based algorithms such as support vector machines have achieved considerable
success in various problems in batch setting, where all of the training data is available in …