This book provides a blend of Matrix and Linear Algebra Theory, Analysis, Differential Equations, Optimization, Optimal and Robust Control. It contains an advanced mathematical tool which serves as a fundamental basis for both instructors and students who study or actively work in Modern Automatic Control or in its applications. It is...

Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function.

This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers....

In recent years, serine proteases and matrix metalloproteinases (MMPs) have gained considerable attention in tumor biology. For most of these proteases, their expression is a reliable indication of ongoing tissue remodeling. This book provides a comprehensive evaluation of the mechanisms of action of proteases and their inhibitors in tumor...

Ideal as a reference or quick review of the fundamentals of linear algebra, this book offers a matrix-oriented approach--with more emphasis on Euclidean n-space, problem solving, and applications, and less emphasis on abstract vector spaces. It features a variety of applications, boxed statements of important...

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most...

This book presents the state of the art in parallel numerical algorithms, applications, architectures, and system software. The book examines various solutions for issues of concurrency, scale, energy efficiency, and programmability, which are discussed in the context of a diverse range of applications. Features: includes contributions from...