This is largely due to 1) a lack of communication and collaboration between the machine
learning research community and natural scientists, 2) a lack of communication about …

The complexity of modern problems turns increasingly larger in industrial environments, so
the classical process monitoring techniques have to adapt to deal with those problems. This …

Just like electricity, Machine Learning will revolutionize our life in many ways–some of which
are not even conceivable today. This book provides a thorough conceptual understanding of …

In China, floods are considered as the most frequent natural disaster responsible for severe
economic losses and serious damages recorded in agriculture and urban infrastructure …

Statistical and now machine learning prediction methods have been gaining popularity in
the field of landslide susceptibility modeling. Particularly, these data driven approaches …

Oil spill discharges from operational maritime activities like ships, oil rigs and other
structures, leaking pipelines, as well as natural hydrocarbon seepage pose serious threats …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility map** is a collection of landslide causative factors …

This chapter introduces the general linear model, illustrating how it subsumes a variety of
standard applied models. It also introduces random vectors and matrices and the …