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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.



Adaptive Signal Processing

Adaptive Signal Processing Author Tülay Adali
ISBN-10 0470575743
Release 2010-06-25
Pages 350
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Leading experts present the latest research results in adaptive signal processing Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing. Then, the challenges of multichannel processing of complex-valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension. Examines the seven most important topics in adaptive filtering that will define the next-generation adaptive filtering solutions Introduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real-life data: non-Gaussianity, non-circularity, non-stationarity, and non-linearity Features self-contained chapters, numerous examples to clarify concepts, and end-of-chapter problems to reinforce understanding of the material Contains contributions from acknowledged leaders in the field Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering.



Statistical learning theory

Statistical learning theory Author Vladimir Naumovich Vapnik
ISBN-10 UOM:39076002704257
Release 1998-09-30
Pages 736
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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.



Neural Networks and Statistical Learning

Neural Networks and Statistical Learning Author Ke-Lin Du
ISBN-10 9781447155713
Release 2013-12-09
Pages 824
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Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.



Neural Networks

Neural Networks Author Simon Haykin
ISBN-10 0780334949
Release 1999-01
Pages 700
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Neural Networks has been writing in one form or another for most of life. You can find so many inspiration from Neural Networks also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Neural Networks book for free.



Intelligent Adaptive Systems

Intelligent Adaptive Systems Author Ming Hou
ISBN-10 9781466517240
Release 2014-12-02
Pages 331
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As ubiquitous as the atmosphere, intelligent adaptive systems (IASs) surround us in our daily lives. When designed well, these systems sense users and their environments so that they can provide support in a manner that is not only responsive to the evolving situation, but unnoticed by the user. A synthesis of recent research and developments on IASs from the human factors (HF) and human–computer interaction (HCI) domains, Intelligent Adaptive Systems: An Interaction-Centered Design Perspective provides integrated design guidance and recommendations for researchers and system developers. The book explores a recognized lack of integration between the HF and HCI research communities, which has led to inconsistencies between the research approaches adopted, and a lack of exploitation of research from one field by the other. The authors integrate theories and methodologies from these domains to provide design recommendations for human–machine developers. They then establish design guidance through the review of conceptual frameworks, analytical methodologies, and design processes for intelligent adaptive systems. The book draws on case studies from the military, medical, and distance learning domains to illustrate intelligent system design to examine lessons learned. Outlining an interaction-centered perspective for designing an IAS, the book details methodologies for understanding human work in complex environments and offers understanding about why and how optimizing human–machine interaction should be central to the design of IASs. The authors present an analytical and design methodology as well as an implementation strategy that helps you choose the proper design framework for your needs.



Reinforcement Learning

Reinforcement Learning Author Richard S. Sutton
ISBN-10 9781461536185
Release 2012-12-06
Pages 172
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Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.



Computational Modeling and Simulation of Intellect Current State and Future Perspectives

Computational Modeling and Simulation of Intellect  Current State and Future Perspectives Author Igelnik, Boris
ISBN-10 9781609605520
Release 2011-05-31
Pages 686
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"This book confronts the problem of meaning by fusing together methods specific to different fields and exploring the computational efficiency and scalability of these methods"--Provided by publisher.



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.



Advances in Nonlinear Signal and Image Processing

Advances in Nonlinear Signal and Image Processing Author Stephen Marshall
ISBN-10 9789775945372
Release 2006
Pages 361
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Advances in Nonlinear Signal and Image Processing has been writing in one form or another for most of life. You can find so many inspiration from Advances in Nonlinear Signal and Image Processing also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Advances in Nonlinear Signal and Image Processing book for free.



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.



Wireless Communication Systems

Wireless Communication Systems Author Ke-Lin Du
ISBN-10 9781139485760
Release 2010-04-15
Pages
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This practically-oriented, all-inclusive guide covers all the major enabling techniques for current and next-generation cellular communications and wireless networking systems. Technologies covered include CDMA, OFDM, UWB, turbo and LDPC coding, smart antennas, wireless ad hoc and sensor networks, MIMO, and cognitive radios, providing readers with everything they need to master wireless systems design in a single volume. Uniquely, a detailed introduction to the properties, design, and selection of RF subsystems and antennas is provided, giving readers a clear overview of the whole wireless system. It is also the first textbook to include a complete introduction to speech coders and video coders used in wireless systems. Richly illustrated with over 400 figures, and with a unique emphasis on practical and state-of-the-art techniques in system design, rather than on the mathematical foundations, this book is ideal for graduate students and researchers in wireless communications, as well as for wireless and telecom engineers.



Kernel Methods and Machine Learning

Kernel Methods and Machine Learning Author S. Y. Kung
ISBN-10 9781139867634
Release 2014-04-17
Pages
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Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.



Cognitive Dynamic Systems

Cognitive Dynamic Systems Author Simon Haykin
ISBN-10 9780521114363
Release 2012-03-22
Pages 309
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A groundbreaking book from Simon Haykin, setting out the fundamental ideas and highlighting a range of future research directions.



Digital Image and Signal Processing for Measurement Systems

Digital Image and Signal Processing for Measurement Systems Author Dr. Richard J. Duro
ISBN-10 9788792982797
Release 2012-06-12
Pages 306
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Digital Image and Signal Processing for Measurement Systems concentrates on signal processing for measurement systems and its objective is to provide a general overview of the area and an appropriate introduction to the topics considered. This is achieved through 10 chapters devoted to current topics of research addressed by different research groups within this area. These 10 chapters reflect advances corresponding to signals of different dimensionality. They go from mostly one dimensional signals in what would be the most traditional area of signal processing realm to RGB signals and to signals of very high dimensionality such as hyperspectral signals that can go up to dimensionalities of more than one thousand. The chapters have been thought out to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area. They provide an overview of some of the problems in the area of signal and image processing for measurement systems and the approaches and techniques that relevant research groups within this area are employing to try to solve them which, in many instances are the state of the art of some of these topics.



Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning Author Christopher M. Bishop
ISBN-10 1493938436
Release 2016-08-23
Pages 738
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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.



Multimedia Signal Processing

Multimedia Signal Processing Author Saeed V. Vaseghi
ISBN-10 0470066490
Release 2007-10-22
Pages 680
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Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines. This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters. Part 2: Model-based Signal Processing covers probability and information models, Bayesian inference, Wiener filter, adaptive filters, linear prediction hidden Markov models and independent component analysis. Part 3: Applications of Signal Processing in Speech, Music and Telecommunications explains the topics of speech and music processing, echo cancellation, deconvolution and channel equalization, and mobile communication signal processing. Covers music signal processing, explains the anatomy and psychoacoustics of hearing and the design of MP3 music coder Examines speech processing technology including speech models, speech coding for mobile phones and speech recognition Covers single-input and multiple-inputs denoising methods, bandwidth extension and the recovery of lost speech packets in applications such as voice over IP (VoIP) Illustrated throughout, including numerous solved problems, Matlab experiments and demonstrations Companion website features Matlab and C++ programs with electronic copies of all figures. This book is ideal for researchers, postgraduates and senior undergraduates in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be a valuable text to professional engineers in telecommunications and audio and signal processing industries.