Описание: Offers a multi-disciplinary and integrated explanation of risk assessment procedures that enables non-specialist readers to gain insights into the development of risk analysis procedures. This book is suitable for practising engineers and graduate engineering students across a range of disciplines.
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 8353 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Автор: Mitchell Название: Machine Learning ISBN: 0071154671 ISBN-13(EAN): 9780071154673 Издательство: McGraw-Hill Рейтинг: Цена: 8386 р. Наличие на складе: Поставка под заказ.
Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
Описание: Explores how we judge engineering education in order to effectively redesign courses and programs that will prepare new engineers for various professional and academic careers This book considers the functions of assessment and its measurement in enginee
Описание: Environmental Systems Engineering and Economics emphasizes the application of optimization, economics, and systems engineering to problems in environmental resources management. This senior level/graduate textbook introduces optimization theory and algorithms that have been successful in resolving water quality and groundwater management problems. Both linear programming and nonlinear optimization are presented. Multiobjective optimization and the linked simulation-optimization (LSO) methodology are also introduced. The basic principles of economics and engineering economics are also discussed to provide a framework for economic decision making. This text contains numerous example problems. Case studies are presented that address water resources management issues in the north China plain, the control of saltwater intrusion in Jakarta, Indonesia, and groundwater resources management in the Yun Lin basin, Taiwan.
Описание: This book is devoted to reporting innovative and significant progress in fuzzy system engineering. Given the maturation of fuzzy logic, this book is dedicated to exploring the recent breakthroughs in fuzziness and soft computing in favour of intelligent system engineering. This monograph presents novel developments of the fuzzy theory as well as interesting applications of the fuzzy logic exploiting the theory to engineer intelligent systems.
Описание: This book constitutes the refereed proceedings of the 8th International Conference on Model Driven Engineering Languages and Systems (formerly the UML series of conferences), MoDELS 2005, held in Montego Bay, Jamaica, in October 2005.The 52 revised full papers and 2 keynote abstracts presented were carefully reviewed and selected from an initial submission of 215 abstracts and 166 papers. The papers are organized in topical sections on process modelling, product families and reuse, state/behavioral modeling, aspects, design strategies, model transformations, model refactoring, quality control, MDA automation, UML 2.0, industrial experience, crosscutting concerns, modeling strategies, as well as a recapitulatory section on workshops, tutorials and panels.
Описание: System modeling and analysis is a standard activity in every engineering discipline. This text offers a broad-based introduction to engineering systems, incorporating material from mechanical, electrical, aerospace, and chemical engineering. The overall theme that distinguishes the text from others is its unified treatment of disparate physical systems, emphasizing similarities in both the modeling and behaviour of lumped-element systems. Linear graph theory provides the framework for modeling engineering systems as lumped elements. The analysis of system dynamics that follows is organized by behavioral characteristics rather than by engineering subdisciplines. Next, the Laplace transform is introduced as a tool for understanding frequency response. The final chapter covers feedback systems. Every chapter includes a wide variety of examples, as well as exercise problems, drawn from real-world mechanical, electrical, hydraulic, chemical, and thermal systems. Aimed at second and third year undergraduates, this introductory text offers a unified entry to the multidisciplinary world of engineering.
Описание: This text contains several articles taking different viewpoints in the field of intelligent systems. There are two branches of Machine Intelligence: Artificial Intelligence and Computational Intelligence, as confirmed by the huge number of published scientific results.
Описание: This book provides a systematic and rigorous presentation of the theory of transportation systems and of the main methodologies for its application to transportation system engineering. Topics are presented with an increasing level of detail and complexity. Different selections of subjects can be used as the basis for undergraduate and postgraduate courses in transportation system engineering as well as economics and regional sciences. Audience: Academic researchers, workers of federal and governmental agencies, consultants and professionals involved in transportation modeling and planning. Academic and Technical libraries. `This book provides a rigorous and comprehensive coverage of transportation models and planning methods and is a must-have to anyone in the transportation community, including students, teachers, and practitioners.' Moshe Ben-Akiva, Massachusetts Institute of Technology, Edmund K. Turner Professor of Civil and Environmental Engineering, Director, Intelligent Transportation Systems. `This book excels in a modern, timely and up-to-date presentation of models used in transportation planning and analysis. It also has the merit of covering both demand modeling and network modeling. It is likely to be an excellent text for teaching and research purposes.' Michael Florian Dr. Eng. Sc., Professor, MRSC (Member of the Royal Society of Canada).
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>
Описание: From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, this book provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals, surveys contemporary challenges, and details cutting-edge machine learning and data mining techniques. This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures and more than 40 case studies help readers visualize the workflow of complex techniques.
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