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A Theory Of Learning And Generalization
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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.
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 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 The Principles of Deep Learning Theory by : Daniel A. Roberts
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.
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 A Theory of Generalization in Learning Machines with Neural Network Applications by : Changfeng Wang
Download or read book A Theory of Generalization in Learning Machines with Neural Network Applications written by Changfeng Wang and published by . This book was released on 1994 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Models of Neural Networks III by : Eytan Domany
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 2012-12-06 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.
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 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 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 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 Guided by Generalization and Uncertainty by : Charley Wu
Download or read book Guided by Generalization and Uncertainty written by Charley Wu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Generalization With Deep Learning: For Improvement On Sensing Capability by : Zhenghua Chen
Download or read book Generalization With Deep Learning: For Improvement On Sensing Capability written by Zhenghua Chen and published by World Scientific. This book was released on 2021-04-07 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.
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 Computational Learning Theory by : Paul Vitanyi
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.
Book Synopsis Algorithms for Reinforcement Learning by : Csaba Grossi
Download or read book Algorithms for Reinforcement Learning written by Csaba Grossi and published by Springer Nature. This book was released on 2022-05-31 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration
