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.
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.
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.
Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
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.
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 227 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.
Download or read book Computational Learning Theory written by Paul Vitanyi and published by Springer Science & Business Media. This book was released on 1995-02-23 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March 1995. The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.
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.
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 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.
Download or read book Models of Neural Networks III written by Eytan Domany and published by Springer Science & Business Media. This book was released on 1996 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a collection of articles by leading researchers in neural networks. This work focuses on data storage and retrieval, and the recognition of handwriting.
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.
Download or read book Statistical Learning Theory written by Vladimir Naumovich Vapnik and published by . This book was released on 1998 with total page 736 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.
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.
Download or read book MICAI 2002 Advances in Artificial Intelligence written by Carlos Coello Coello and published by Springer Science & Business Media. This book was released on 2002-03-27 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Mérida, Yucatán, Mexico in April 2002. The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.
Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Download or read book Rough Sets Fuzzy Sets Data Mining and Granular Computing written by Guoyin Wang and published by Springer Science & Business Media. This book was released on 2003-05-08 with total page 758 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2003, held in Chongqing, China in May 2003. The 39 revised full papers and 75 revised short papers presented together with 2 invited keynote papers and 11 invited plenary papers were carefully reviewed and selected from a total of 245 submissions. The papers are organized in topical sections on rough sets foundations and methods; fuzzy sets and systems; granular computing; neural networks and evolutionary computing; data mining, machine learning, and pattern recognition; logics and reasoning; multi-agent systems; and Web intelligence and intelligent systems.
