Information Theory and Statistical Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Information Theory and Statistical Learning write by Frank Emmert-Streib. This book was released on 2009. Information Theory and Statistical Learning available in PDF, EPUB and Kindle. This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
Information Theory, Inference and Learning Algorithms
Information Theory, Inference and Learning Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Information Theory, Inference and Learning Algorithms write by David J. C. MacKay. This book was released on 2003-09-25. Information Theory, Inference and Learning Algorithms available in PDF, EPUB and Kindle. Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
The Nature of Statistical Learning Theory
The Nature of Statistical Learning Theory - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Nature of Statistical Learning Theory write by Vladimir Vapnik. This book was released on 2013-06-29. The Nature of Statistical Learning Theory available in PDF, EPUB and Kindle. The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Algebraic Geometry and Statistical Learning Theory
Algebraic Geometry and Statistical Learning Theory - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algebraic Geometry and Statistical Learning Theory write by Sumio Watanabe. This book was released on 2009-08-13. Algebraic Geometry and Statistical Learning Theory available in PDF, EPUB and Kindle. Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.
Reliable Reasoning
Reliable Reasoning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Reliable Reasoning write by Gilbert Harman. This book was released on 2012-01-13. Reliable Reasoning available in PDF, EPUB and Kindle. The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.
