This chapter discusses the method of Kernel Ridge Regression, which is a very simple
special case of Support Vector Regression. The main formula of the method is identical to a …

Abstract Machine learning was used to develop a model that had the capability to predict
yields of solid products from biomass torrefaction using input features of biomass properties …

Novel applications in unstructured and non-stationary human environments require robots
that learn from experience and adapt autonomously to changing conditions. Predictive …

An accurate and reliable forecast for fault/degradation trend has an enormous significance
in predictive maintenance of industrial systems. Prognosis of bearing is essential for efficient …

This research investigates the predictive modeling of a dataset containing parameters
denoted by r (m), z (m), and T (K) which is temperature. The considered process is a …

Kernel online convex optimization (KOCO) is a framework combining the expressiveness of
non-parametric kernel models with the regret guarantees of online learning. First-order …

Online kernel learning (OKL) is a flexible framework to approach prediction problems, since
the large approximation space provided by reproducing kernel Hilbert spaces can contain …

In oil and gas exploration, elucidating the complex interdependencies among geological
variables is paramount. Our study introduces the application of sophisticated regression …

This study reports on how ML algorithms are employed to investigate and predict surface
roughness. Experiments were executed with a CNC milling machine, using AA7075 as part …

We revisit the sequential variants of linear regression with the squared loss, classification
problems with hinge loss, and logistic regression, all characterized by unbounded losses in …