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Machine Learning Paradigms, Maria Virvou; Efthimios Alepis; George A. Tsihrint


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Автор: Maria Virvou; Efthimios Alepis; George A. Tsihrint
Название:  Machine Learning Paradigms
ISBN: 9783030137427
Издательство: Springer
Классификация:



ISBN-10: 3030137422
Обложка/Формат: Hardcover
Страницы: 223
Вес: 0.52 кг.
Дата издания: 2020
Серия: Intelligent Systems Reference Library
Язык: English
Издание: 1st ed. 2020
Иллюстрации: 29 illustrations, color; 31 illustrations, black and white; xvi, 223 p. 60 illus., 29 illus. in color.
Размер: 163 x 242 x 22
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Подзаголовок: Advances in Learning Analytics
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:• Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;• Using learning analytics to predict student performance;• Using learning analytics to create learning materials and educational courses; and• Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.


Data Mining. Practical Machine Learning Tools and Techniques, 4 ed.

Автор: Witten, Ian H.
Название: Data Mining. Practical Machine Learning Tools and Techniques, 4 ed.
ISBN: 0128042915 ISBN-13(EAN): 9780128042915
Издательство: Elsevier Science
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Цена: 9262.00 р.
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Описание:

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.

Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.

It contains

  • Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
  • Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
  • Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

  • Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
  • Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
  • Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
  • Includes open-access online courses that introduce practical applications of the material in the book
Computer Age Statistical Inference

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 9029.00 р.
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

An Introduction to Machine Learning

Автор: Miroslav Kubat
Название: An Introduction to Machine Learning
ISBN: 3319348868 ISBN-13(EAN): 9783319348865
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.

Machine Learning Paradigms

Автор: Tsihrintzis
Название: Machine Learning Paradigms
ISBN: 3319940295 ISBN-13(EAN): 9783319940298
Издательство: Springer
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Цена: 23757.00 р.
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Описание: Data Analytics in the Medical, Biological and Signal Sciences.- Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics.- Classification Methods in Image Analysis with a Special Focus on Medical Analytics.- Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field.- Machine Learning Methods for the Protein Fold Recognition Problem.

Machine Learning Paradigms: Theory and Application

Автор: Aboul Ella Hassanien
Название: Machine Learning Paradigms: Theory and Application
ISBN: 3030023567 ISBN-13(EAN): 9783030023560
Издательство: Springer
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Цена: 22359.00 р.
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Описание:

The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms.
The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.
Machine Learning Paradigms

Автор: George A. Tsihrintzis; Dionisios N. Sotiropoulos;
Название: Machine Learning Paradigms
ISBN: 3030067777 ISBN-13(EAN): 9783030067779
Издательство: Springer
Рейтинг:
Цена: 23757.00 р.
Наличие на складе: Поставка под заказ.

Описание: This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities.The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences.Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics.This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

Computational Intelligence Paradigms in Economic and Financial Decision Making

Автор: Marina Resta
Название: Computational Intelligence Paradigms in Economic and Financial Decision Making
ISBN: 331921439X ISBN-13(EAN): 9783319214399
Издательство: Springer
Рейтинг:
Цена: 15672.00 р.
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Описание: The book focuses on a set of cutting-edge research techniques, highlighting the potential of soft computing tools in the analysis of economic and financial phenomena and in providing support for the decision-making process.

Computational Intelligence Paradigms in Economic and Financial Decision Making

Автор: Marina Resta
Название: Computational Intelligence Paradigms in Economic and Financial Decision Making
ISBN: 3319365258 ISBN-13(EAN): 9783319365251
Издательство: Springer
Рейтинг:
Цена: 11753.00 р.
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Описание: The book focuses on a set of cutting-edge research techniques, highlighting the potential of soft computing tools in the analysis of economic and financial phenomena and in providing support for the decision-making process.

Paradigms in Cartography

Автор: Pablo Iv?n Az?car Fern?ndez; Manfred Ferdinand Buc
Название: Paradigms in Cartography
ISBN: 3642447546 ISBN-13(EAN): 9783642447549
Издательство: Springer
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Цена: 14365.00 р.
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Описание: This book examines the trends, concepts and directions in cartography and mapping in modernism and post-modernism. It explores philosophical and epistemological issues in cartography from positivist-empiricist, neo-positivist and post-structuralist stances.

E-Learning Paradigms and Applications

Автор: Mirjana Ivanovi?; Lakhmi C. Jain
Название: E-Learning Paradigms and Applications
ISBN: 3662509466 ISBN-13(EAN): 9783662509463
Издательство: Springer
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Цена: 15672.00 р.
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Описание: This book presents the state-of-the-art of e-learning and tutoring systems, and discusses their capabilities and benefits that stem from integrating software agents. It includes various case studies to validate the usefulness of the presented paradigms.

Machine Learning Paradigms

Автор: Aristomenis S. Lampropoulos; George A. Tsihrintzis
Название: Machine Learning Paradigms
ISBN: 3319384961 ISBN-13(EAN): 9783319384962
Издательство: Springer
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Цена: 13059.00 р.
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Описание: This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes.

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems

Автор: Tsihrintzis George A., Virvou Maria, Sakkopoulos Evangelos
Название: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems
ISBN: 3030156273 ISBN-13(EAN): 9783030156275
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.


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