Forecasting sea level is critical for coastal structure building and port operations. There are,
however, challenges in making these predictions, resulting from the complicated processes …

An in-depth understanding of uncertainty is the first step to making effective decisions under
uncertainty. Machine/deep learning (ML/DL) has been hugely leveraged to solve complex …

This research presents the optimum performance model for predicting the shallow
foundation ultimate bearing capacity (UBC). Twenty-one models are employed, trained …

We provide a new unifying view, including all existing proper probabilistic sparse
approximations for Gaussian process regression. Our approach relies on expressing the …

We present a new Gaussian process (GP) regression model whose covariance is
parameterized by the the locations of M pseudo-input points, which we learn by a gradient …

The present research introduces an optimum performance soft computing model by
comparing deep (multi-layer perceptron neural network, support vector machine, least …

This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …

Gaussian processes (GPs) are flexible, simple to implement, fully probabilistic methods
suitable for a wide range of problems in regression and classification. A recent overview of …

The expressive power of Bayesian kernel-based methods has led them to become an
important tool across many different facets of artificial intelligence, and useful to a plethora of …

We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-
MDP), a framework for modeling families of related tasks using low-dimensional latent …