Download or read book The Adaptive Web written by Peter Brusilovski and published by Springer Science & Business Media. This book was released on 2007-04-24 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt: This state-of-the-art survey provides a systematic overview of the ideas and techniques of the adaptive Web and serves as a central source of information for researchers, practitioners, and students. The volume constitutes a comprehensive and carefully planned collection of chapters that map out the most important areas of the adaptive Web, each solicited from the experts and leaders in the field.
Download or read book Complex Valued Nonlinear Adaptive Filters written by Danilo P. Mandic and published by John Wiley & Sons. This book was released on 2009-04-20 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.
Download or read book Adaptive Filtering written by Lino Garcia Morales and published by BoD – Books on Demand. This book was released on 2013-02-20 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive filtering can be used to characterize unknown systems in time-variant environments. The main objective of this approach is to meet a difficult comprise: maximum convergence speed with maximum accuracy. Each application requires a certain approach which determines the filter structure, the cost function to minimize the estimation error, the adaptive algorithm, and other parameters; and each selection involves certain cost in computational terms, that in any case should consume less time than the time required by the application working in real-time. Theory and application are not, therefore, isolated entities but an imbricated whole that requires a holistic vision. This book collects some theoretical approaches and practical applications in different areas that support expanding of adaptive systems.
Download or read book Adaptive Learning Methods for Nonlinear System Modeling written by Danilo Comminiello and published by Butterworth-Heinemann. This book was released on 2018-06-11 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. - Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. - Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. - Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Download or read book Soft Computing and Signal Processing written by Jiacun Wang and published by Springer. This book was released on 2019-02-13 with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book includes research papers on current developments in the field of soft computing and signal processing, selected from papers presented at the International Conference on Soft Computing and Signal Processing (ICSCSP 2018). It features papers on current topics, such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms and machine learning. It also discusses various aspects of these topics, like technologies, product implementation, and application issues.
Download or read book EEG Signal Processing and Machine Learning written by Saeid Sanei and published by John Wiley & Sons. This book was released on 2021-09-23 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: EEG Signal Processing and Machine Learning Explore cutting edge techniques at the forefront of electroencephalogram research and artificial intelligence from leading voices in the field The newly revised Second Edition of EEG Signal Processing and Machine Learning delivers an inclusive and thorough exploration of new techniques and outcomes in electroencephalogram (EEG) research in the areas of analysis, processing, and decision making about a variety of brain states, abnormalities, and disorders using advanced signal processing and machine learning techniques. The book content is substantially increased upon that of the first edition and, while it retains what made the first edition so popular, is composed of more than 50% new material. The distinguished authors have included new material on tensors for EEG analysis and sensor fusion, as well as new chapters on mental fatigue, sleep, seizure, neurodevelopmental diseases, BCI, and psychiatric abnormalities. In addition to including a comprehensive chapter on machine learning, machine learning applications have been added to almost all the chapters. Moreover, multimodal brain screening, such as EEG-fMRI, and brain connectivity have been included as two new chapters in this new edition. Readers will also benefit from the inclusion of: A thorough introduction to EEGs, including neural activities, action potentials, EEG generation, brain rhythms, and EEG recording and measurement An exploration of brain waves, including their generation, recording, and instrumentation, abnormal EEG patterns and the effects of ageing and mental disorders A treatment of mathematical models for normal and abnormal EEGs Discussions of the fundamentals of EEG signal processing, including statistical properties, linear and nonlinear systems, frequency domain approaches, tensor factorization, diffusion adaptive filtering, deep neural networks, and complex-valued signal processing Perfect for biomedical engineers, neuroscientists, neurophysiologists, psychiatrists, engineers, students and researchers in the above areas, the Second Edition of EEG Signal Processing and Machine Learning will also earn a place in the libraries of undergraduate and postgraduate students studying Biomedical Engineering, Neuroscience and Epileptology.
Download or read book Efficient Nonlinear Adaptive Filters written by Haiquan Zhao and published by Springer Nature. This book was released on 2023-02-10 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the design, analysis, and application of nonlinear adaptive filters with the goal of improving efficient performance (ie the convergence speed, steady-state error, and computational complexity). The authors present a nonlinear adaptive filter, which is an important part of nonlinear system and digital signal processing and can be applied to diverse fields such as communications, control power system, radar sonar, etc. The authors also present an efficient nonlinear filter model and robust adaptive filtering algorithm based on the local cost function of optimal criterion to overcome non-Gaussian noise interference. The authors show how these achievements provide new theories and methods for robust adaptive filtering of nonlinear and non-Gaussian systems. The book is written for the scientist and engineer who are not necessarily an expert in the specific nonlinear filtering field but who want to learn about the current research and application. The book is also written to accompany a graduate/PhD course in the area of nonlinear system and adaptive signal processing.
Download or read book Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and published by Now Publishers Inc. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
Download or read book Signal Processing and Machine Learning Theory written by Paulo S.R. Diniz and published by Elsevier. This book was released on 2023-07-10 with total page 1236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc. - Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools - Presents core principles in signal processing theory and shows their applications - Discusses some emerging signal processing tools applied in machine learning methods - References content on core principles, technologies, algorithms and applications - Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Download or read book Proceedings of the 1st Workshop on Deep Learning for Recommender Systems written by Alexandros Karatzoglou and published by . This book was released on 2016-09-15 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: Workshop on Deep Learning for Recommender Systems Sep 15, 2016-Sep 15, 2016 Boston, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Download or read book Collaborative Filtering Using Data Mining and Analysis written by Bhatnagar, Vishal and published by IGI Global. This book was released on 2016-07-13 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.
Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer Science & Business Media. This book was released on 2010-10-21 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt: The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.
Download or read book Cooperative and Graph Signal Processing written by Petar Djuric and published by Academic Press. This book was released on 2018-07-04 with total page 868 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience. With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings. - Presents the first book on cooperative signal processing and graph signal processing - Provides a range of applications and application areas that are thoroughly covered - Includes an editor in chief and associate editor from the IEEE Transactions on Signal Processing and Information Processing over Networks who have recruited top contributors for the book
Download or read book Advanced Intelligent Systems for Sustainable Development AI2SD 2020 written by Janusz Kacprzyk and published by Springer Nature. This book was released on 2022-02-07 with total page 1173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book publishes the best papers accepted and presented at the 3rd edition of the International Conference on Advanced Intelligent Systems for Sustainable Development Applied to Agriculture, Energy, Health, Environment, Industry, Education, Economy, and Security (AI2SD’2020). This conference is one of the biggest amalgamations of eminent researchers, students, and delegates from both academia and industry where the collaborators have an interactive access to emerging technology and approaches globally. In this book, readers find the latest ideas addressing technological issues relevant to all areas of the social and human sciences for sustainable development. Due to the nature of the conference with its focus on innovative ideas and developments, the book provides the ideal scientific and brings together very high-quality chapters written by eminent researchers from different disciplines, to discover the most recent developments in scientific research.
Download or read book Adaptive Filtering and Change Detection written by Fredrik Gustafsson and published by John Wiley & Sons. This book was released on 2000-10-03 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive filtering is a branch of digital signal processing which enables the selective enhancement of desired elements of a signal and the reduction of undesired elements. Change detection is another kind of adaptive filtering for non-stationary signals, and is the basic tool in fault detection and diagnosis. This text takes the unique approach that change detection is a natural extension of adaptive filtering, and the broad coverage encompasses both the mathematical tools needed for adaptive filtering and change detection and the applications of the technology. Real engineering applications covered include aircraft, automotive, communication systems, signal processing and automatic control problems. The unique integration of both theory and practical applications makes this book a valuable resource combining information otherwise only available in separate sources Comprehensive coverage includes many examples and case studies to illustrate the ideas and show what can be achieved Uniquely integrates applications to airborne, automotive and communications systems with the essential mathematical tools Accompanying Matlab toolbox available on the web illustrating the main ideas and enabling the reader to do simulations using all the figures and numerical examples featured This text would prove to be an essential reference for postgraduates and researchers studying digital signal processing as well as practising digital signal processing engineers.
Download or read book Signal Processing Techniques for Knowledge Extraction and Information Fusion written by Danilo Mandic and published by Springer Science & Business Media. This book was released on 2008-03-23 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the latest research achievements from signal processing and related disciplines, consolidating existing and proposed directions in DSP-based knowledge extraction and information fusion. The book includes contributions presenting both novel algorithms and existing applications, emphasizing on-line processing of real-world data. Readers discover applications that solve biomedical, industrial, and environmental problems.
