естественные науки

книги по философии

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This Companion brings together a team of leading figures in contemporary philosophy to provide an in-depth exposition and analysis of Quine’s extensive influence across philosophy’s many subfields, highlighting the breadth of his work, and revealing his continued significance today. Provides an in-depth account and analysis of W.V.O. Quine’s contribution to American Philosophy, and his position as one of the late twentieth-century’s most influential analytic philosophers Brings together newly-commissioned essays by leading figures within contemporary philosophy Covers Quine’s work across philosophy of logic, philosophy of language, ontology and metaphysics, epistemology, and more Explores his work in relation to the origins of analytic philosophy in America, and to the history of philosophy more broadly Highlights the breadth of Quine’s work across the discipline, and demonstrates the continuing influence of his work within the philosophical community 

A Companion to W. V. O. Quine

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

Harman Gilbert