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A Theory Of Learning And Generalization
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Author :Mathukumalli Vidyasagar Publisher :Springer Science & Business Media ISBN 13 :1447137485 Total Pages :498 pages Book Rating :4.4/5 (471 download)
Book Synopsis Learning and Generalisation by : Mathukumalli Vidyasagar
Download or read book Learning and Generalisation written by Mathukumalli Vidyasagar and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Book Synopsis A Theory of Learning and Generalization by : Mathukumalli Vidyasagar
Download or read book A Theory of Learning and Generalization written by Mathukumalli Vidyasagar and published by Springer. This book was released on 1997 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Theory of Learning and Generalization provides a formal mathematical theory for addressing intuitive questions of the type: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the output of a previously unseen input? How much training is required to achieve a specified level of accuracy in the prediction? How can one "identify" the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? This is the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side by side leads to new insights, as well as new results in both topics. An extensive references section and open problems will help readers to develop their own work in the field.
Book Synopsis The Nature of Statistical Learning Theory by : Vladimir Vapnik
Download or read book The Nature of Statistical Learning Theory written by Vladimir Vapnik and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Generalization of Knowledge by : Marie T. Banich
Download or read book Generalization of Knowledge written by Marie T. Banich and published by Psychology Press. This book was released on 2011-01-07 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume takes a multidisciplinary perspective on generalization of knowledge from several fields associated with Cognitive Science, including Cognitive Neuroscience, Computer Science, Education, Linguistics, Developmental Science, and Speech, Language and Hearing Sciences. The aim is to derive general principles from triangulation across different disciplines and approaches.
Book Synopsis Perceptual Learning by : Manfred Fahle
Download or read book Perceptual Learning written by Manfred Fahle and published by MIT Press. This book was released on 2002 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perceptual learning is the specific and relatively permanent modification of perception and behaviour following sensory experience. This book presents advances made during the 1990s in this rapidly growing field.
Download or read book Learning Theory written by Felipe Cucker and published by Cambridge University Press. This book was released on 2007-03-29 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines.
Book Synopsis Experience, Variation and Generalization by : Inbal Arnon
Download or read book Experience, Variation and Generalization written by Inbal Arnon and published by John Benjamins Publishing. This book was released on 2011-07-20 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are all children exposed to the same linguistic input, and do they follow the same route in acquisition? The answer is no: The language that children hear differs even within a social class or cultural setting, as do the paths individual children take. The linguistic signal itself is also variable, both within and across speakers - the same sound is different across words; the same speech act can be realized with different constructions. The challenge here is to explain, given their diversity of experience, how children arrive at similar generalizations about their first language. This volume brings together studies of phonology, morphology, and syntax in development, to present a new perspective on how experience and variation shape children's linguistic generalizations. The papers deal with variation in forms, learning processes, and speaker features, and assess the impact of variation on the mechanisms and outcomes of language learning.
Book Synopsis Introduction to Psychology by : Jennifer Walinga
Download or read book Introduction to Psychology written by Jennifer Walinga and published by Hasanraza Ansari. This book was released on with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to help students organize their thinking about psychology at a conceptual level. The focus on behaviour and empiricism has produced a text that is better organized, has fewer chapters, and is somewhat shorter than many of the leading books. The beginning of each section includes learning objectives; throughout the body of each section are key terms in bold followed by their definitions in italics; key takeaways, and exercises and critical thinking activities end each section.
Book Synopsis Algebraic Geometry and Statistical Learning Theory by : Sumio Watanabe
Download or read book Algebraic Geometry and Statistical Learning Theory written by Sumio Watanabe and published by Cambridge University Press. This book was released on 2009-08-13 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Mathematics Of Generalization by : David. H Wolpert
Download or read book The Mathematics Of Generalization written by David. H Wolpert and published by CRC Press. This book was released on 2018-03-05 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.
Book Synopsis Information-Theoretic Methods in Data Science by : Miguel R. D. Rodrigues
Download or read book Information-Theoretic Methods in Data Science written by Miguel R. D. Rodrigues and published by Cambridge University Press. This book was released on 2021-04-08 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.
Book Synopsis Learning Theory and Behavior by : Orval Hobart Mowrer
Download or read book Learning Theory and Behavior written by Orval Hobart Mowrer and published by Legare Street Press. This book was released on 2023-07-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the theories and practices of behaviorism in this insightful book by noted psychologist Hobart Orval Mowrer. From the basics of conditioning to the complex issues surrounding motivation and reinforcement, this book provides a thorough overview of the underlying principles that govern human behavior. Whether you're a psychology student or simply interested in understanding human behavior, Learning Theory and Behavior is an essential addition to your library. This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Book Synopsis Statistical Learning Theory by : Vladimir Naumovich Vapnik
Download or read book Statistical Learning Theory written by Vladimir Naumovich Vapnik and published by Wiley-Interscience. This book was released on 1998-09-30 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book Synopsis Direct Instruction by : Siegfried Engelmann
Download or read book Direct Instruction written by Siegfried Engelmann and published by Educational Technology. This book was released on 1980 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Teaching for Transfer by : Anne McKeough
Download or read book Teaching for Transfer written by Anne McKeough and published by Routledge. This book was released on 2013-12-16 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: The transfer of learning is universally accepted as the ultimate aim of teaching. Facilitating knowledge transfer has perplexed educators and psychologists over time and across theoretical frameworks; it remains a central issue for today's practitioners and theorists. This volume examines the reasons for past failures and offers a reconceptualization of the notion of knowledge transfer, its problems and limitations, as well as its possibilities. Leading scholars outline programs of instruction that have effectively produced transfer at a variety of levels from kindergarten to university. They also explore a broad range of issues related to learning transfer including conceptual development, domain-specific knowledge, learning strategies, communities of learners, and disposition. The work of these contributors epitomizes theory-practice integration and enables the reader to review the reciprocal relation between the two that is so essential to good theorizing and effective teaching.
Book Synopsis Reliable Reasoning by : Gilbert Harman
Download or read book Reliable Reasoning written by Gilbert Harman and published by MIT Press. This book was released on 2012-01-13 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Encyclopedia of the Sciences of Learning by : Norbert M. Seel
Download or read book Encyclopedia of the Sciences of Learning written by Norbert M. Seel and published by Springer Science & Business Media. This book was released on 2011-10-05 with total page 3643 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.
