Author : Mathukumalli Vidyasagar
Publisher : Springer Science & Business Media
Page : 498 pages
File Size : 14,62 MB
Release : 2013-03-14
Category : Technology & Engineering
ISBN : 1447137485
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.
Author : Mathukumalli Vidyasagar
Publisher : Springer
Page : 408 pages
File Size : 33,52 MB
Release : 1997
Category : Computers
ISBN :
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.
Author : Vladimir Vapnik
Publisher : Springer Science & Business Media
Page : 324 pages
File Size : 44,19 MB
Release : 2013-06-29
Category : Mathematics
ISBN : 1475732643
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.
Author : Marie T. Banich
Publisher : Psychology Press
Page : 380 pages
File Size : 17,31 MB
Release : 2011-01-07
Category : Education
ISBN : 1136945466
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.
Author : Felipe Cucker
Publisher : Cambridge University Press
Page : pages
File Size : 24,39 MB
Release : 2007-03-29
Category : Computers
ISBN : 1139462865
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.
Author : Inbal Arnon
Publisher : John Benjamins Publishing
Page : 312 pages
File Size : 23,59 MB
Release : 2011-07-20
Category : Language Arts & Disciplines
ISBN : 9027285047
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.
Author : Anne McKeough
Publisher : Routledge
Page : 247 pages
File Size : 16,82 MB
Release : 2013-12-16
Category : Education
ISBN : 1135444226
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.
Author : Jennifer Walinga
Publisher : Hasanraza Ansari
Page : 810 pages
File Size : 25,83 MB
Release :
Category : Body, Mind & Spirit
ISBN :
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.
Author : Miguel R. D. Rodrigues
Publisher : Cambridge University Press
Page : 561 pages
File Size : 15,85 MB
Release : 2021-04-08
Category : Computers
ISBN : 1108427138
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.
Author : Sumio Watanabe
Publisher : Cambridge University Press
Page : 295 pages
File Size : 29,48 MB
Release : 2009-08-13
Category : Computers
ISBN : 0521864674
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.
