Tsihrintzis George A., Virvou Maria, Sakkopoulos Evangelos Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems 9783030156275

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Автор: Tsihrintzis George A., Virvou Maria, Sakkopoulos Evangelos
Название: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems
ISBN: 9783030156275
Издательство: Springer
Классификация:

Медицинская генетика

Базы данных

Искусственный интеллект

Машинное обучение

ISBN-10: 3030156273
Обложка/Формат: Hardcover
Страницы: 548
Вес: 0.97 кг.
Дата издания: 15.07.2019
Серия: Learning and analytics in intelligent systems
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 101 illustrations, color; 38 illustrations, black and white; xx, 548 p. 139 illus., 101 illus. in color.
Размер: 234 x 156 x 32
Читательская аудитория: Professional & vocational
Подзаголовок: Applications of learning and analytics in intelligent systems
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
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.
Дополнительное описание: Chapter 1: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems.- Chapter 2: A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure.- Chapter 3: Differential gene Expression Analysis of RNA-se

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems, Tsihrintzis George A., Virvou Maria, Sakkopoulos Evangelos