Authors introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. DLC: Machine learning.

This text covers the basic concepts and insights of game theory and offers classic examples and applications in a concise format. It provides a serious treatment of contract - an important, but often neglected topic in game theory - with minimal departure from the standard game coverage. Without skimping on mathematical precision, this text presents concepts and applications us...(展开全部)

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 important modeling and prediction techniques, a...(展开全部)

How and why are languages constantly changing? Historical linguistics seeks to find out by going beyond the history of individual languages to discover the general principles which underlie language change. But our evidence is severely limited. Most of the world's languages are still unwritten, and even in areas with long written traditions, such as Europe and the Near East, do...(展开全部)

The first part of this introduction to ergodic theory addresses measure-preserving transformations of probability spaces and covers such topics as recurrence properties and the Birkhoff ergodic theorem. The second part focuses on the ergodic theory of continuous transformations of compact metrizable spaces. Several examples are detailed, and the final chapter outlines results a...(展开全部)

The first part of this introduction to ergodic theory addresses measure-preserving transformations of probability spaces and covers such topics as recurrence properties and the Birkhoff ergodic theorem. The second part focuses on the ergodic theory of continuous transformations of compact metrizable spaces. Several examples are detailed, and the final chapter outlines results a...(展开全部)

Professor Ito is one of the most distinguished probability theorists in the world, and in this modern, concise introduction to the subject he explains basic probabilistic concepts rigorously and yet gives at the same time an intuitive understanding of random phenomena. In the first chapter he considers finite situations, but from an advanced standpoint that enables the transiti...(展开全部)

This volume is an introduction to mathematical Knot Theory; the theory of knots and links of simple closed curves in three-dimensional space. It consists of a selection of topics which graduate students have found to be a successful introduction to the field. Three distinct techniques are employed; Geometric Topology Manoeuvres, Combinatorics, and Algebraic Topology. Each topic...(展开全部)