Computational Learning Theory And Natural Learning Systems Selecting Good Models

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Computational Learning Theory and Natural Learning Systems: Making learning systems practical

Author : Russell Greiner
Publisher : MIT Press
ISBN 13 : 9780262571180
Total Pages : 440 pages
Book Rating : 4.5/5 (711 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems: Making learning systems practical by : Russell Greiner

Download or read book Computational Learning Theory and Natural Learning Systems: Making learning systems practical written by Russell Greiner and published by MIT Press. This book was released on 1994 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and Ǹatural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems. Contributors : Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E.M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S.V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador.

Computational Learning Theory and Natural Learning Systems

Author : Stephen José Hanson
Publisher :
ISBN 13 :
Total Pages : 550 pages
Book Rating : 4.:/5 (93 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems by : Stephen José Hanson

Download or read book Computational Learning Theory and Natural Learning Systems written by Stephen José Hanson and published by . This book was released on 1994 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Boosting

Author : Robert E. Schapire
Publisher : MIT Press
ISBN 13 : 0262526034
Total Pages : 544 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Boosting by : Robert E. Schapire

Download or read book Boosting written by Robert E. Schapire and published by MIT Press. This book was released on 2014-01-10 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

Algorithmic Learning Theory

Author : Shai Ben David
Publisher : Springer Science & Business Media
ISBN 13 : 3540233563
Total Pages : 519 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Algorithmic Learning Theory by : Shai Ben David

Download or read book Algorithmic Learning Theory written by Shai Ben David and published by Springer Science & Business Media. This book was released on 2004-09-23 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.

Foundations of Machine Learning, second edition

Author : Mehryar Mohri
Publisher : MIT Press
ISBN 13 : 0262351366
Total Pages : 505 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri

Download or read book Foundations of Machine Learning, second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Cost-Sensitive Machine Learning

Author : Balaji Krishnapuram
Publisher : CRC Press
ISBN 13 : 143983928X
Total Pages : 316 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Cost-Sensitive Machine Learning by : Balaji Krishnapuram

Download or read book Cost-Sensitive Machine Learning written by Balaji Krishnapuram and published by CRC Press. This book was released on 2011-12-19 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect

Advancing Socio-Economics

Author : Karl H. Müller
Publisher : Rowman & Littlefield
ISBN 13 : 9780742511774
Total Pages : 470 pages
Book Rating : 4.5/5 (117 download)

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Book Synopsis Advancing Socio-Economics by : Karl H. Müller

Download or read book Advancing Socio-Economics written by Karl H. Müller and published by Rowman & Littlefield. This book was released on 2005 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this landmark volume, J. Rodgers Hollingsworth, Karl H. M ller, and Ellen Jane Hollingsworth take a first step towards imposing order on the increasingly diverse field of socio-economics by embedding the various disciplines and sub-disciplines in a common core. The distinguished contributors in this volume show how institutions, governance arrangements, societal sectors, organizations, individual actors, and innovativeness are intertwined and, ultimately, how individuals and firms have a high degree of autonomy. By offering original suggestions and guidelines for developing a socio-economics research agenda focused on institutional analysis, Advancing Socio-Economics: An Institutionalist Perspective, will enlighten all interested in the social sciences.

Proceedings of the ... Annual ACM Conference on Computational Learning Theory

Author :
Publisher :
ISBN 13 :
Total Pages : 356 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Proceedings of the ... Annual ACM Conference on Computational Learning Theory by :

Download or read book Proceedings of the ... Annual ACM Conference on Computational Learning Theory written by and published by . This book was released on 1999 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Proceedings of the ... Annual Conference on Computational Learning Theory

Author :
Publisher :
ISBN 13 :
Total Pages : 348 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Proceedings of the ... Annual Conference on Computational Learning Theory by :

Download or read book Proceedings of the ... Annual Conference on Computational Learning Theory written by and published by . This book was released on 1999 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Probably Approximately Correct

Author : Leslie Valiant
Publisher : Basic Books (AZ)
ISBN 13 : 0465032710
Total Pages : 210 pages
Book Rating : 4.4/5 (65 download)

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Book Synopsis Probably Approximately Correct by : Leslie Valiant

Download or read book Probably Approximately Correct written by Leslie Valiant and published by Basic Books (AZ). This book was released on 2013-06-04 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting a theory of the theoryless, a computer scientist provides a model of how effective behavior can be learned even in a world as complex as our own, shedding new light on human nature.

Learning to Learn

Author : Sebastian Thrun
Publisher : Springer Science & Business Media
ISBN 13 : 1461555299
Total Pages : 346 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Learning to Learn by : Sebastian Thrun

Download or read book Learning to Learn written by Sebastian Thrun and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.

Artificial Intelligence Tools

Author : Diego Galar Pascual
Publisher : CRC Press
ISBN 13 : 1498760198
Total Pages : 472 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Artificial Intelligence Tools by : Diego Galar Pascual

Download or read book Artificial Intelligence Tools written by Diego Galar Pascual and published by CRC Press. This book was released on 2015-04-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of e

Semi-Supervised Learning

Author : Olivier Chapelle
Publisher : MIT Press
ISBN 13 : 0262514125
Total Pages : 525 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Semi-Supervised Learning by : Olivier Chapelle

Download or read book Semi-Supervised Learning written by Olivier Chapelle and published by MIT Press. This book was released on 2010-01-22 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

Adaptive and Natural Computing Algorithms

Author : Bernadete Ribeiro
Publisher : Springer Science & Business Media
ISBN 13 : 3211273891
Total Pages : 561 pages
Book Rating : 4.2/5 (112 download)

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Book Synopsis Adaptive and Natural Computing Algorithms by : Bernadete Ribeiro

Download or read book Adaptive and Natural Computing Algorithms written by Bernadete Ribeiro and published by Springer Science & Business Media. This book was released on 2005-12-12 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ICANNGA series of Conferences has been organised since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientific community and is a reference for established workers in this area. Starting in Innsbruck, in Austria (1993), then to Ales in Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague in the Czech Republic (2001) and finally Roanne, in France (2003), the ICANNGA series has established itself for experienced workers in the field. The series has also been of value to young researchers wishing both to extend their knowledge and experience and also to meet internationally renowned experts. The 2005 Conference, the seventh in the ICANNGA series, will take place at the University of Coimbra in Portugal, drawing on the experience of previous events, and following the same general model, combining technical sessions, including plenary lectures by renowned scientists, with tutorials.

Anticipatory Behavior in Adaptive Learning Systems

Author : Giovanni Pezzulo
Publisher : Springer Science & Business Media
ISBN 13 : 3642025641
Total Pages : 345 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Anticipatory Behavior in Adaptive Learning Systems by : Giovanni Pezzulo

Download or read book Anticipatory Behavior in Adaptive Learning Systems written by Giovanni Pezzulo and published by Springer Science & Business Media. This book was released on 2009-06-15 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anticipatory behavior in adaptive learning systems continues attracting attention of researchers in many areas, including cognitive systems, neuroscience, psychology, and machine learning. This book constitutes the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2008, held in Munich, Germany, in June 2008, in collaboration with the six-monthly Meeting of euCognition 'The Role of Anticipation in Cognition'. The 18 revised full papers presented were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The introductory chapter of this state-of-the-art survey not only provides an overview of the contributions included in this volume but also revisits the current available terminology on anticipatory behavior and relates it to the available system approaches. The papers are organized in topical sections on anticipation in psychology with focus on the ideomotor view, conceptualizations, anticipation and dynamical systems, computational modeling of psychological processes in the individual and social domains, behavioral and cognitive capabilities based on anticipation, and computational frameworks and algorithms for anticipation, and their evaluation.

Explainable AI and Other Applications of Fuzzy Techniques

Author : Julia Rayz
Publisher : Springer Nature
ISBN 13 : 3030820998
Total Pages : 506 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Explainable AI and Other Applications of Fuzzy Techniques by : Julia Rayz

Download or read book Explainable AI and Other Applications of Fuzzy Techniques written by Julia Rayz and published by Springer Nature. This book was released on 2021-07-27 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques. This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI—in short, to anyone who is interested in problems involving fuzziness and AI in general.

Multisector Insights in Healthcare, Social Sciences, Society, and Technology

Author : Burrell, Darrell Norman
Publisher : IGI Global
ISBN 13 :
Total Pages : 409 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Multisector Insights in Healthcare, Social Sciences, Society, and Technology by : Burrell, Darrell Norman

Download or read book Multisector Insights in Healthcare, Social Sciences, Society, and Technology written by Burrell, Darrell Norman and published by IGI Global. This book was released on 2024-02-27 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to a variety of global challenges in recent times, the dissolution of traditional boundaries between academic disciplines has given rise to a pressing need for innovative problem-solving. Complex issues affect our societies, spanning healthcare, social sciences, organizational behavior, and technology. This shifting landscape necessitates a comprehensive exploration into the interconnections between these diverse fields. The book, Multisector Insights in Healthcare, Social Sciences, Society, and Technology, is an innovative guide that seeks to examine the relationships between various fields of knowledge. It celebrates the transformative impact of applied research and interdisciplinary collaboration as the driving force behind overcoming the most significant challenges of our time. As the boundaries between disciplines blur, the book takes readers on a journey through multifaceted issues at the intersection of healthcare, social sciences, organizational behavior, and technology. Chapters within this book unravel the complexities of healthcare ethics, global health initiatives, organizational dynamics, and technological advancements. Through literature reviews, qualitative and quantitative studies, and real-world case analyses, the compendium not only identifies the problems but also offers concrete, evidence-backed solutions. This interdisciplinary approach underscores the need to address the pressing challenges of our time, emphasizing the need for collaborative strategies to drive positive change.