Machine Learning for Multimedia Content Analysis, Yihong Gong; Wei Xu
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 0387310738 ISBN-13(EAN): 9780387310732 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 18622.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Автор: William Herring Название: Learning Radiology: Recognizing the Basics, 3rd Edition ISBN: 0323328075 ISBN-13(EAN): 9780323328074 Издательство: Elsevier Science Рейтинг: Цена: 6336.00 р. Наличие на складе: Нет в наличии.
Описание: A must-have for anyone who will be required to read and interpret common radiologic images, Learning Radiology: Recognizing the Basics is an image-filled, practical, and easy-to-read introduction to key imaging modalities. Skilled radiology teacher William Herring, MD, masterfully covers exactly what you need to know to effectively interpret medical images of all modalities. Learn the latest on ultrasound, MRI, CT, patient safety, dose reduction, radiation protection, and more, in a time-friendly format with brief, bulleted text and abundant high-quality images.
Then ensure your mastery of the material with additional online content, bonus images, and self-assessment exercises at Student Consult. Identify a wide range of common and uncommon conditions based upon their imaging findings. Arrive at diagnoses by following a pattern recognition approach, and logically overcome difficult diagnostic challenges with the aid of decision trees.
Quickly grasp the fundamentals you need to know through more than 700 images and an easy-to-use format and pedagogy, including: bolding of key points and icons designating special content; Diagnostic Pitfalls; Really, Really Important Points; Weblinks; and Take-Home Points. Gauge your mastery of the material and build confidence with extra images, bonus content, interactive self-assessment exercises, and USMLE-style Q&A that provide effective chapter review and quick practice for your exams. Apply the latest recommendations on patient safety, dose reduction and radiation protection Benefit from the extensive knowledge and experience of esteemed author Dr.
William Herring-a skilled radiology teacher and the host of his own specialty website, www.learningradiology.com. Stay current in the latest advancements and developments with meticulous updates throughout including a new chapter on Pediatric Radiology as well as more than 60 new and updated photos, many highlighting newer imaging modalities. Maximize your learning experience with interactive Student Consult extras videos/images of 3D images, functional imaging examinations, dynamic studies, and additional assessments.
Student Consult eBook version included with purchase. This enhanced eBook experience allows you to search all of the text, figures, references, and videos from the book on a variety of devices.
Автор: Malley Название: Statistical Learning for Biomedical Data ISBN: 0521699096 ISBN-13(EAN): 9780521699099 Издательство: Cambridge Academ Рейтинг: Цена: 6494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques.
Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.
Автор: Ohm Название: Multimedia Content Analysis ISBN: 3662528266 ISBN-13(EAN): 9783662528266 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook covers the theoretical backgrounds and practical aspects of image, video and audio feature expression, e.g., color, texture, edge, shape, salient point and area, motion, 3D structure, audio/sound in time, frequency and cepstral domains, structure and melody. Up-to-date algorithms for estimation, search, classification and compact expression of feature data are described in detail. Concepts of signal decomposition (such as segmentation, source tracking and separation), as well as composition, mixing, effects, and rendering, are discussed. Numerous figures and examples help to illustrate the aspects covered. The book was developed on the basis of a graduate-level university course, and most chapters are supplemented by problem-solving exercises. The book is also a self-contained introduction both for researchers and developers of multimedia content analysis systems in industry.
Автор: Nicu Sebe; Yuncai Liu; Yueting Zhuang; Thomas S. H Название: Multimedia Content Analysis and Mining ISBN: 3540734163 ISBN-13(EAN): 9783540734161 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the International Workshop on Multimedia Content Analysis and Mining, MCAM 2007, held in Weihai, China in June/July 2007. This book covers a wide range of topics, including various aspects of multimedia in entertainment, commerce, science, medicine, and public safety.
Автор: Matthieu Cord; P?draig Cunningham Название: Machine Learning Techniques for Multimedia ISBN: 3642443621 ISBN-13(EAN): 9783642443626 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Автор: Ajay Divakaran Название: Multimedia Content Analysis ISBN: 1441945679 ISBN-13(EAN): 9781441945679 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Capturing the most significant milestones in the multimedia field, this book consists of invited chapters that cover the entire range of the field. It includes unique coverage of multimedia content analysis based products described by pioneers in the field.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
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