Applications of optimization and machine learning in image processing and iot,
Автор: Brunton, Steven L. (university Of Washington) Kutz Название: Data-driven science and engineering ISBN: 1009098489 ISBN-13(EAN): 9781009098489 Издательство: Cambridge Academ Рейтинг: Цена: 7918.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.
Автор: Dalrymple, Brian Название: Forensic Digital Imaging Processing ISBN: 1498743439 ISBN-13(EAN): 9781498743433 Издательство: Taylor&Francis Рейтинг: Цена: 17762.00 р. 22202.00-20% Наличие на складе: Есть (1 шт.) Описание: The digital revolution over the past several decades has advanced every facet of evidence detection, photography, optimization, and interpretation. Forensic scientists and practitioners have benefited tremendously from the move from film to digital. With proper procedures in place, digital images and casework capabilities have increased tremendously in both complexity and range due to a vast array of tools to enhance evidence and photography. Forensic Digital Image Processing: Optimization of Impression Evidence provides the forensic investigator with the tools and understanding to extract, optimize, and interpret the maximum evidence possible from crime scenes to increase identifications. The book begins by examining the emergence of forensic digital image processing, and the gradual improvement and acceptance of the science over the past four decades. Coverage includes looking at the issues of image integrity and authentication including forensic image optimization and the manipulation of images.? Chapters explore techniques exploiting color theory, modes, and channels to optimize signal-to-noise ratio in images. One of the greatest assets of digital image technology is the ability to combine multiple images of the same subject to create a final, blended image: one that displays the desired evidence and is especially useful for fingerprint or footwear impression. Later chapters demonstrate image subtraction, focus stacking, and high dynamic range, utilizing images in optimum focus and with substrate interference diminished or removed entirely. The authors look at fast Fourier transform as an optimal tool for noise removal, addressing basic theory and diagnosis of the noise signatures. The book discusses the history of digital imaging techniques and their treatment within the court system. Forensic Digital Image Processing: Optimization of Impression Evidence serves as an invaluable resource and tool for practicing professionals–as well as those new to the field—to look at best practices, the latest technology, and advances in utilizing the increasing array of tools of the trade.
Название: Optimization for machine learning ISBN: 0262537761 ISBN-13(EAN): 9780262537766 Издательство: MIT Press Рейтинг: Цена: 13794.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: Roman Garnett Название: Bayesian Optimization ISBN: 110842578X ISBN-13(EAN): 9781108425780 Издательство: Cambridge Academ Рейтинг: Цена: 7445.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
Описание: This book constitutes the thoroughly refereed post-conference proceedings of the International Dagstuhl-Seminar on Efficient Algorithms for Global Optimization Methods in Computer Vision, held in Dagstuhl Castle, Germany, in November 2011.
Описание: This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016.
Описание: This book provides a platform for exploring nature-inspired optimization techniques in the context of imaging applications. Optimization has become part and parcel of all computational vision applications, and since the amount of data used in these applications is vast, the need for optimization techniques has increased exponentially. These accuracy and complexity are a major area of concern when it comes to practical applications. However, these optimization techniques have not yet been fully explored in the context of imaging applications. By presenting interdisciplinary concepts, ranging from optimization to image processing, the book appeals to a broad readership, while also encouraging budding engineers to pursue and employ innovative nature-inspired techniques for image processing applications.
Автор: A, Vasuki Название: Nature Inspired Optimization for Image Processing ISBN: 0367255987 ISBN-13(EAN): 9780367255985 Издательство: Taylor&Francis Рейтинг: Цена: 19140.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book is a lucid description of fifteen of the existing important optimization algorithms that are based on swarm intelligence and superior in performance. Nature has a rich abundance of flora and fauna that inspired the development of nature inspired optimization techniques
Автор: Dalrymple, Brian Название: Forensic Digital Image Processing ISBN: 0367778769 ISBN-13(EAN): 9780367778767 Издательство: Taylor&Francis Рейтинг: Цена: 10411.00 р. Наличие на складе: Нет в наличии.
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