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Information Theoretic Learning

Information Theoretic Learning Author Jose C. Principe
ISBN-10 1441915702
Release 2010-04-06
Pages 448
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This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

Artificial Neural Networks and Machine Learning ICANN 2012

Artificial Neural Networks and Machine Learning    ICANN 2012 Author Alessandro Villa
ISBN-10 9783642332661
Release 2012-09-19
Pages 590
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The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.

Intelligent Information Processing VIII

Intelligent Information Processing VIII Author Zhongzhi Shi
ISBN-10 9783319483900
Release 2016-11-09
Pages 278
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This book constitutes the refereed proceedings of the 9th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2016, held in Melbourne, VIC, Australia, in October 2016. The 24 full papers and 3 short papers presented were carefully reviewed and selected from more than 40 submissions. They are organized in topical sections on machine learning, data mining, deep learning, social computing, semantic web and text processing, image understanding, and brain-machine collaboration.

Information Loss in Deterministic Signal Processing Systems

Information Loss in Deterministic Signal Processing Systems Author Bernhard C. Geiger
ISBN-10 9783319595337
Release 2017-08-04
Pages 145
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This book introduces readers to essential tools for the measurement and analysis of information loss in signal processing systems. Employing a new information-theoretic systems theory, the book analyzes various systems in the signal processing engineer’s toolbox: polynomials, quantizers, rectifiers, linear filters with and without quantization effects, principal components analysis, multirate systems, etc. The user benefit of signal processing is further highlighted with the concept of relevant information loss. Signal or data processing operates on the physical representation of information so that users can easily access and extract that information. However, a fundamental theorem in information theory—data processing inequality—states that deterministic processing always involves information loss. These measures form the basis of a new information-theoretic systems theory, which complements the currently prevailing approaches based on second-order statistics, such as the mean-squared error or error energy. This theory not only provides a deeper understanding but also extends the design space for the applied engineer with a wide range of methods rooted in information theory, adding to existing methods based on energy or quadratic representations.

Frontiers of Higher Order Fuzzy Sets

Frontiers of Higher Order Fuzzy Sets Author Alireza Sadeghian
ISBN-10 9781461434429
Release 2014-12-03
Pages 261
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Frontiers of Higher Order Fuzzy Sets, provides a unified representation theorem for higher order fuzzy sets. The book elaborates on the concept of gradual elements and their integration with the higher order fuzzy sets. This book also is devoted to the introduction of new frameworks based on general T2FSs, IT2FSs, Gradual elements, Shadowed sets and rough sets. Such new frameworks will provide more capable frameworks for real applications. Applications of higher order fuzzy sets in various fields will be discussed. In particular, the properties and characteristics of the new proposed frameworks would be studied. Such frameworks that are the result of the integration of general T2FSs, IT2FSs, gradual elements, shadowed sets and rough sets will be shown to be suitable to be applied in the fields of bioinformatics, business, management, ambient intelligence, medicine, cloud computing and smart grids.

Similarity Based Pattern Recognition

Similarity Based Pattern Recognition Author Edwin Hancock
ISBN-10 9783642391408
Release 2013-06-28
Pages 297
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This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013, which was held in York, UK, in July 2013. The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practical applications, and offer a timely picture of the state of the art in the field.

Differential Geometrical Theory of Statistics

Differential Geometrical Theory of Statistics Author Frédéric Barbaresco
ISBN-10 9783038424246
Release 2018-04-06
Pages 472
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This book is a printed edition of the Special Issue "Differential Geometrical Theory of Statistics" that was published in Entropy

Elements of Information Theory

Elements of Information Theory Author Thomas M. Cover
ISBN-10 9781118585771
Release 2012-11-28
Pages 792
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The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Signals and Images

Signals and Images Author Rosângela Fernandes Coelho
ISBN-10 9781498722377
Release 2015-11-04
Pages 598
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Signals and Images: Advances and Results in Speech, Estimation, Compression, Recognition, Filtering, and Processing cohesively combines contributions from field experts to deliver a comprehensive account of the latest developments in signal processing. These experts detail the results of their research related to audio and speech enhancement, acoustic image estimation, video compression, biometric recognition, hyperspectral image analysis, tensor decomposition with applications in communications, adaptive sparse-interpolated filtering, signal processing for power line communications, bio-inspired signal processing, seismic data processing, arithmetic transforms for spectrum computation, particle filtering in cooperative networks, three-dimensional television, and more. This book not only shows how signal processing theory is applied in current and emerging technologies, but also demonstrates how to tackle key problems such as how to enhance speech in the time domain, improve audio quality, and meet the desired electrical consumption target for controlling carbon emissions. Signals and Images: Advances and Results in Speech, Estimation, Compression, Recognition, Filtering, and Processing serves as a guide to the next generation of signal processing solutions for speech and video coding, hearing aid devices, big data processing, smartphones, smart digital communications, acoustic sensors, and beyond.

Quantum Information Processing with Finite Resources

Quantum Information Processing with Finite Resources Author Marco Tomamichel
ISBN-10 9783319218915
Release 2015-10-14
Pages 138
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This book provides the reader with the mathematical framework required to fully explore the potential of small quantum information processing devices. As decoherence will continue to limit their size, it is essential to master the conceptual tools which make such investigations possible. A strong emphasis is given to information measures that are essential for the study of devices of finite size, including Rényi entropies and smooth entropies. The presentation is self-contained and includes rigorous and concise proofs of the most important properties of these measures. The first chapters will introduce the formalism of quantum mechanics, with particular emphasis on norms and metrics for quantum states. This is necessary to explore quantum generalizations of Rényi divergence and conditional entropy, information measures that lie at the core of information theory. The smooth entropy framework is discussed next and provides a natural means to lift many arguments from information theory to the quantum setting. Finally selected applications of the theory to statistics and cryptography are discussed. The book is aimed at graduate students in Physics and Information Theory. Mathematical fluency is necessary, but no prior knowledge of quantum theory is required.

Information Theory in Computer Vision and Pattern Recognition

Information Theory in Computer Vision and Pattern Recognition Author Francisco Escolano Ruiz
ISBN-10 9781848822979
Release 2009-07-14
Pages 364
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Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.

Neural and adaptive systems

Neural and adaptive systems Author José C. Príncipe
ISBN-10 0471351679
Release 2000
Pages 656
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Develop New Insight into the Behavior of Adaptive Systems This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction. The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live,' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation. Key Features of the Text * The text and CD combine to become an interactive learning tool. * Emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations. * Each key concept is followed by an interactive example. * Over 200 fully functional simulations of adaptive systems are included. * The text and CD offer a unified view of neural networks, adaptive filters, pattern recognition, and support vector machines. * Hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts. The CD-ROM Contains: * A complete, electronic version of the text in hypertext format * NeuroSolutions, an industry standard, icon-based neural network/adaptive system simulator * A tutorial on how to use NeuroSolutions * Additional data files to use with the simulator "An innovative approach to describing neurocomputing and adaptive learning systems from a perspective which unifies classical linear adaptive systems approaches with the modern advances in neural networks. It is rich in examples and practical insight." -James Zeidler, University of California, San Diego

Introduction to Neural Engineering for Motor Rehabilitation

Introduction to Neural Engineering for Motor Rehabilitation Author Dario Farina
ISBN-10 9781118628638
Release 2013-05-21
Pages 600
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The only book that covers in detail a broad range of cutting-edge topics within motor rehabilitation technology Neural engineering is a discipline that uses engineering techniques to understand, repair, replace, enhance, or treat diseases of neural systems. This book describes state-of-the-art methods within this field, from brain-computer interfaces to spinal and cortical plasticity. Touching on electrode design, signal processing, the neurophysiology of movement, robotics, and much more, this innovative book presents the latest information for readers working in biomedical engineering. Each section of Introduction to Neural Engineering for Motor Rehabilitation begins with an overview of techniques before moving on to provide information on the most recent findings. Topics include: INJURIES OF THE NERVOUS SYSTEM—including diseases and injuries of the central nervous system leading to sensory-motor impairment; peripheral and spinal plasticity after nerve injuries; and motor control modules of human movement in health and disease SIGNAL DETECTION AND CONDITIONING—including progress in peripheral neural interfaces; multi-modal, multi-site neuronal recordings for brain research; methods for non-invasive electroencephalograph detection; wavelet denoising and conditioning of neural recordings FUNCTION REPLACEMENT (Prostheses and Orthosis)—including an introduction to upper limb prosthetics; controlling prostheses using peripheral nerve stimulation invasive interfaces for amputees; and exoskeletal robotics for functional substitution FUNCTION RESTORATION—including methods for movement restoration; advanced user interfaces for upper limb functional electrical stimulation; and selectivity of peripheral neural interfaces REHABILITATION THROUGH NEUROMODULATION—including brain-computer interface applied to motor recovery after brain injury; functional electrical therapy of upper extremities; and robotic assisted neurorehabilitation Introduction to Neural Engineering for Motor Rehabilitation is an important textbook and reference for graduate students and researchers in the fields of biomedical and neural engineering.

Kernel Adaptive Filtering

Kernel Adaptive Filtering Author Weifeng Liu
ISBN-10 9781118211212
Release 2011-09-20
Pages 240
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Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm Presents a powerful model-selection method called maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their growing structure Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site Concludes each chapter with a summary of the state of the art and potential future directions for original research Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.

Statistical and Machine Learning Approaches for Network Analysis

Statistical and Machine Learning Approaches for Network Analysis Author Matthias Dehmer
ISBN-10 9781118346983
Release 2012-06-26
Pages 352
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Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

The IEEE 2000 Adaptive Systems for Signal Processing Communications and Control Symposium AS SPCC October 1 4 2000 Chateau Lake Louise Lake Louise Alberta Canada

The IEEE 2000 Adaptive Systems for Signal Processing  Communications  and Control Symposium   AS SPCC   October 1 4  2000  Chateau Lake Louise  Lake Louise  Alberta  Canada Author Simon S. Haykin
ISBN-10 0780358007
Release 2000
Pages 480
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The IEEE 2000 Adaptive Systems for Signal Processing Communications and Control Symposium AS SPCC October 1 4 2000 Chateau Lake Louise Lake Louise Alberta Canada has been writing in one form or another for most of life. You can find so many inspiration from The IEEE 2000 Adaptive Systems for Signal Processing Communications and Control Symposium AS SPCC October 1 4 2000 Chateau Lake Louise Lake Louise Alberta Canada also informative, and entertaining. Click DOWNLOAD or Read Online button to get full The IEEE 2000 Adaptive Systems for Signal Processing Communications and Control Symposium AS SPCC October 1 4 2000 Chateau Lake Louise Lake Louise Alberta Canada book for free.

Statistical Mechanics of Driven Diffusive Systems

Statistical Mechanics of Driven Diffusive Systems Author
ISBN-10 0080538746
Release 1995-07-24
Pages 220
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Far-from-equilibrium phenomena, while abundant in nature, are not nearly as well understood as their equilibrium counterparts. On the theoretical side, progress is slowed by the lack of a simple framework, such as the Boltzmann-Gbbs paradigm in the case of equilibrium thermodynamics. On the experimental side, the enormous structural complexity of real systems poses serious obstacles to comprehension. Similar difficulties have been overcome in equilibrium statistical mechanics by focusing on model systems. Even if they seem too simplistic for known physical systems, models give us considerable insight, provided they capture the essential physics. They serve as important theoretical testing grounds where the relationship between the generic physical behavior and the key ingredients of a successful theory can be identified and understood in detail. Within the vast realm of non-equilibrium physics, driven diffusive systems form a subset with particularly interesting properties. As a prototype model for these systems, the driven lattice gas was introduced roughly a decade ago. Since then, a number of surprising phenomena have been discovered including singular correlations at generic temperatures, as well as novel phase transitions, universality classes, and interfacial instabilities. This book summarizes current knowledge on driven systems, from apedagogical discussion of the original driven lattice gas to a brief survey of related models. Given that the topic is far from closed, much emphasis is placed on detailing open questions and unsolved problems as an incentive for the reader to pursue thesubject further. Provides a summary of current knowledge on driven diffusive systems Emphasis is placed on detailing open questions and unsolved problems Covers the entire subject from original driven lattice gas to a survey of related models