Self-Organizing Maps
Self-Organizing Maps

Author: Teuvo Kohonen

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 514

ISBN-13: 3642569277

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The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About 4000 research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real world problems. Many fields of science have adopted the SOM as a standard analytical tool: statistics, signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. This new edition includes a survey of over 2000 contemporary studies to cover the newest results. Case examples are provided with detailed formulae, illustrations, and tables. Further, a new chapter on software tools for SOM has been included whilst other chapters have been extended and reorganised.

Artificial Neural Networks in Medicine and Biology
Artificial Neural Networks in Medicine and Biology

Author: H. Malmgren

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 339

ISBN-13: 1447105133

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This book contains the proceedings of the conference ANNIMAB-l, held 13-16 May 2000 in Goteborg, Sweden. The conference was organized by the Society for Artificial Neural Networks in Medicine and Biology (ANNIMAB-S), which was established to promote research within a new and genuinely cross-disciplinary field. Forty-two contributions were accepted for presentation; in addition to these, S invited papers are also included. Research within medicine and biology has often been characterised by application of statistical methods for evaluating domain specific data. The growing interest in Artificial Neural Networks has not only introduced new methods for data analysis, but also opened up for development of new models of biological and ecological systems. The ANNIMAB-l conference is focusing on some of the many uses of artificial neural networks with relevance for medicine and biology, specifically: • Medical applications of artificial neural networks: for better diagnoses and outcome predictions from clinical and laboratory data, in the processing of ECG and EEG signals, in medical image analysis, etc. More than half of the contributions address such clinically oriented issues. • Uses of ANNs in biology outside clinical medicine: for example, in models of ecology and evolution, for data analysis in molecular biology, and (of course) in models of animal and human nervous systems and their capabilities. • Theoretical aspects: recent developments in learning algorithms, ANNs in relation to expert systems and to traditional statistical procedures, hybrid systems and integrative approaches.

Advances in Neural Information Processing Systems 7
Advances in Neural Information Processing Systems 7

Author: Gerald Tesauro

Publisher: MIT Press

Published: 1995

Total Pages: 1180

ISBN-13: 9780262201049

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November 28-December 1, 1994, Denver, Colorado NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus remains the study of a wide variety of learning algorithms and architectures, for both supervised and unsupervised learning. The 139 contributions are divided into eight parts: Cognitive Science, Neuroscience, Learning Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Visual Processing, and Applications. Topics of special interest include the analysis of recurrent nets, connections to HMMs and the EM procedure, and reinforcement- learning algorithms and the relation to dynamic programming. On the theoretical front, progress is reported in the theory of generalization, regularization, combining multiple models, and active learning. Neuroscientific studies range from the large-scale systems such as visual cortex to single-cell electrotonic structure, and work in cognitive scientific is closely tied to underlying neural constraints. There are also many novel applications such as tokamak plasma control, Glove-Talk, and hand tracking, and a variety of hardware implementations, with particular focus on analog VLSI.

Feed-Forward Neural Networks
Feed-Forward Neural Networks

Author: Jouke Annema

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 248

ISBN-13: 1461523370

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Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.

Government Failure
Government Failure

Author: Gordon Tullock

Publisher: Cato Institute

Published: 2002-05-01

Total Pages: 211

ISBN-13: 1935308009

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When market forces fail us, what are we to do? Who will step in to protect the public interest? The government, right? Wrong. The romantic view of bureaucrats coming to the rescue confuses the true relationship between economics and politics. Politicians often cite "market failure" as justification for meddling with the economy, but a group of leading scholars show the shortcomings of this view. In Government Failure, these scholars explain the school of study known as "public choice," which uses the tools of economics to understand and evaluate government activity. Gordon Tullock, one of the founders of public choice, explains how government "cures" often cause more harm than good. Tullock provides an engaging overview of public choice and discusses how interest groups seek favors from government at enormous costs to society. Displaying the steely realism that has marked public choice, Tullock shows the political world as it is, rather than as it should be. Gordon Brady scrutinizes American public policy, looking closely at international trade, efforts at regulating technology, and environmental policy. At every turn Brady points out the ways in which interest groups have manipulated the government to advance their own agendas. Arthur Seldon, a seminal scholar in public choice, provides a comparative perspective from Great Britain. He examines how government interventions in the British economy have led to inefficiency and warns about the political centralization promised by the European Community. Government Failure heralds a new approach to the study of politics and public policy. This book enlightens readers with the basic concepts of public choice in an unusually accessible way to show the folly of excessive faith in the state.

Self-Organizing Neural Networks
Self-Organizing Neural Networks

Author: Udo Seiffert

Publisher: Physica

Published: 2013-11-11

Total Pages: 289

ISBN-13: 3790818100

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The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up to-date treatment of the field of self-organizing neural networks, which will be ac cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup porting this book and contributing the first chapter.

Industrial Applications Of Neural Networks
Industrial Applications Of Neural Networks

Author: Francoise Fogelman Soulie

Publisher: World Scientific

Published: 1998-01-15

Total Pages: 489

ISBN-13: 9814525375

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This book is a collection of real-world applications of neural networks, which were presented at the ICANN '95 conference of the European Neural Network Society. The contributions have been carefully selected by the Program Committee under three criteria: soundness of the technical approach, relevance for the application sector, and quality of the results obtained.The book covers all major areas of industrial and service activities: process engineering, control and monitoring, technical diagnosis and nondestructive testing, power systems, robotics, transportation, telecommunications, remote sensing, banking, finance and insurance, forecasting, document processing, and medicine. It thus represents one of the most comprehensive existing surveys of the applicability and use of neural networks in industry and services.

Normative Experience in Internet Politics
Normative Experience in Internet Politics

Author: Françoise Massit-Folléa

Publisher: Presses des MINES

Published: 2012

Total Pages: 22

ISBN-13: 2911256573

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The ways in which the Internet is managed and controlled -often labeled as Internet Governance- are usually considered as standing on four main pillars: Technology, Market Laws, State Regulation and Uses. Nevertheless, its specific features, the consequences of the plurality of norms it involves and of the decision-making processes it entails are rarely addressed in a comprehensive analysis. This book explores the Internet’s functioning both as a practical-intellectual experience and a political challenge. By means of several case studies, it proposes a substantial and reflexive treatment of multileveled, formal or informal Internet Politics. The book’s overall endeavor is to outline an understanding of what is -or may be- a “digital common good”. The authors are members of a European academic team gathered by the Vox Internet research program’s meetings. They adopt a multi-disciplinary approach, embedding technological innovation in the field of social sciences (communication studies, sociology, law, political science and philosophy).

Hybrid Neural Systems
Hybrid Neural Systems

Author: Stefan Wermter

Publisher: Springer

Published: 2006-12-30

Total Pages: 411

ISBN-13: 3540464174

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Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems. The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches.