Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download
Автор: Bishop, Christopher M. Название: Neural Networks for Pattern Recognition ISBN: 0198538642 ISBN-13(EAN): 9780198538646 Издательство: Oxford Academ Рейтинг: Цена: 4683 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Providing a comprehensive account of neural networks from a statistical perspective, this book emphasizes on pattern recognition, which represents the area of greatest applicability for neural networks in contemporary times.
Автор: Brian D. Ripley Название: Pattern Recognition and Neural Networks ISBN: 0521717701 ISBN-13(EAN): 9780521717700 Издательство: Cambridge Academ Рейтинг: Цена: 3746 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Now in paperback: the most reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author’s website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.
Автор: Sergios Theodoridis Название: Pattern Recognition, ISBN: 1597492728 ISBN-13(EAN): 9781597492720 Издательство: Elsevier Science Рейтинг: Цена: 8410 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. . · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques. . · Many more diagrams included--now in two color--to provide greater insight through visual presentation. . . · Matlab code of the most common methods are given at the end of each chapter. . . . . . · More Matlab code is available, together with an accompanying manual, via this site . . . . · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. . · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
Описание: This book stems from a unique and highly effective approach in introducing signal processing, instrumentation, diagnostics, filtering, control, and system integration.It presents the interactive industrial grade software testbed of mold oscillator that captures the mold motion distortion induced by coupling of the electro-hydraulic actuator nonlinearity with the resonance of the mold oscillator beam assembly. The testbed is then employed as a virtual lab to generate input-output data records that permit unraveling and refining complex behavior of the actual production system through merging dynamics, signal processing, instrumentation, and control into a coherent problem-solving package.The material is presented in a visually rich, mathematically and graphically well supported, but not analytically overburdened format. By incorporating software testbed into homework and project assignments, the book fully brings out the excitement of going through the adventure of exploring and solving a mold oscillator distortion problem, while covering the key signal processing, diagnostics, instrumentation, modeling, control, and system integration concepts.The approach presented in this book has been supported by two education advancement awards from the College of Engineering of the University of Illinois at Urbana-Champaign.
Описание: This book provides a fundamentally new approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This 'dissimilarity representation' bridges the gap between the traditionally opposing approaches of statistical and structural pattern recognition.Physical phenomena, objects and events in the world are related in various and often complex ways. Such relations are usually modeled in the form of graphs or diagrams. While this is useful for communication between experts, such representation is difficult to combine and integrate by machine learning procedures. However, if the relations are captured by sets of dissimilarities, general data analysis procedures may be applied for analysis.With their detailed description of an unprecedented approach absent from traditional textbooks, the authors have crafted an essential book for every researcher and systems designer studying or developing pattern recognition systems.
Описание: The field of biometrics utilizes computer models of the physical and behavioral characteristics of human beings with a view to reliable personal identification. The human characteristics of interest include visual images, speech, and indeed anything which might help to uniquely identify the individual.The other side of the biometrics coin is biometric synthesis - rendering biometric phenomena from their corresponding computer models. For example, we could generate a synthetic face from its corresponding computer model. Such a model could include muscular dynamics to model the full gamut of human emotions conveyed by facial expressions.This book is a collection of carefully selected papers presenting the fundamental theory and practice of various aspects of biometric data processing in the context of pattern recognition. The traditional task of biometric technologies - human identification by analysis of biometric data - is extended to include the new discipline of biometric synthesis.
Описание: This volume contains the latest in the series of ICAPR proceedings on the state-of-the-art of different facets of pattern recognition. These conferences have already carved out a unique position among events attended by the pattern recognition community. The contributions tackle open problems in the classic fields of image and video processing, document analysis and multimedia object retrieval as well as more advanced topics in biometrics speech and signal analysis. Many of the papers focus both on theory and application driven basic research pattern recognition.
Описание: "Fuzzy Models and Algorithms for Pattern Recognition and Image Processing" presents a comprehensive introduction of the use of fuzzy models in pattern
ition and selected topics in image processing and computer vision. Unique to this volume in the "Kluwer Handbooks of Fuzzy Sets Series" is the fact that this book was written in its
entirety by its four authors. A single notation, presentation style, and purpose are used throughout.
The result is an extensive unified treatment of many fuzzy models for pattern
recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included
are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military
image analysis, and industrial engineering.
Описание: This book constitutes the refereed proceedings of the Second International Conference on Advances in Pattern Recognition, ICAPR 2001, held in Rio de Janeiro, Brazil in March 2001.The 40 revised full papers presented together with three invited papers and two tutorial presentations were carefully reviewed and selected for inclusion in the proceedings. The book is organized in topical sections on neural networks and computational intelligence, character recognition and document analysis, feature selection and analysis, pattern recognition and classification, image and signal processing applications, and image feature analysis and retrieval.
Автор: Kumar Название: Correlation Pattern Recognition ISBN: 0521153484 ISBN-13(EAN): 9780521153485 Издательство: Cambridge Academ Рейтинг: Цена: 4787 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing. This book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. It shows both digital and optical implementations. It also contains technology presented by the team that developed it and includes case studies of significant interest, such as face and fingerprint recognition. Suitable for graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner.
Автор: Sergios Theodoridis Название: Pattern Recognition & Matlab Intro, ISBN: 0123744911 ISBN-13(EAN): 9780123744913 Издательство: Elsevier Science Рейтинг: Цена: 8878 р. Наличие на складе: Невозможна поставка.
Описание: Looks at the classical and the theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided a self-contained volume encapsulating this wide spectrum of information.
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