Gilbert Harman – författare

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.

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.

This volume is a direct result of a conference held at Princeton University to honor George A. Miller, an extraordinary psychologist. A distinguished panel of speakers from various disciplines -- psychology, philosophy, neuroscience and artificial intelligence -- were challenged to respond to Dr. Miller''s query: "What has happened to cognition? In other words, what has the past 30 years contributed to our understanding of the mind? Do we really know anything that wasn''t already clear to William James?" Each participant tried to stand back a little from his or her most recent work, but to address the general question from his or her particular standpoint. The chapters in the present volume derive from that occasion.

This volume is a direct result of a conference held at Princeton University to honor George A. Miller, an extraordinary psychologist. A distinguished panel of speakers from various disciplines -- psychology, philosophy, neuroscience and artificial intelligence -- were challenged to respond to Dr. Miller''s query: "What has happened to cognition? In other words, what has the past 30 years contributed to our understanding of the mind? Do we really know anything that wasn''t already clear to William James?" Each participant tried to stand back a little from his or her most recent work, but to address the general question from his or her particular standpoint. The chapters in the present volume derive from that occasion.

Originally published in 1990. This study argues that scepticism is an intelligible view and that the issue scepticism raises is whether or not certain sceptical hypotheses are as plausible as the ordinary views we accept. It discusses psychological concepts, definitions of knowledge, belief and hypothetic inference (inference to the best explanation). Starting from ‘Is skepticism a problem for epistemology’, the book takes us through the argument for the possibility of scepticism, including looking at sense data and considering memory and perception.

Originally published in 1990. This study argues that scepticism is an intelligible view and that the issue scepticism raises is whether or not certain sceptical hypotheses are as plausible as the ordinary views we accept. It discusses psychological concepts, definitions of knowledge, belief and hypothetic inference (inference to the best explanation). Starting from ‘Is skepticism a problem for epistemology’, the book takes us through the argument for the possibility of scepticism, including looking at sense data and considering memory and perception.