Описание: This book provides a comprehensive introduction to rough set-based feature selection. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle.
Автор: Zhao Haitao, Lai Zhihui, Leung Henry Название: Feature Learning and Understanding: Algorithms and Applications ISBN: 3030407934 ISBN-13(EAN): 9783030407933 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses current topics in rough set theory. Since Pawlak`s rough set theory was first proposed to offer a basis for imprecise and uncertain data and reasoning from data, many workers have investigated its foundations and applications.
Описание: This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms.The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book. This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Описание: This book introduces a range of image color feature extraction techniques. In addition, the book can be used as an introduction to image color feature techniques for those who are new to the research field and software.
Knowledge Capital that ensures sustainability, competitiveness and stability of an organization can be regenerated in value-added form and made available for the creation of quality products and services by application of Knowledge Management System (KMS), across a diverse range of fields. Knowledge Management Systems: Concepts, Technologies and Practices focuses upon the standard procedures and technologies underlying the development of a KMS, while discussing some novel concepts like Virtuous KM Cycle, MASK techniques, Daisy Model and AI-KM Model.
Описание: This book presents a detailed study on fractional-order, set-point, weighted PID control strategies and the development of curve-fitting-based approximation techniques for fractional-order parameters.
Описание: This book explores key examples concerning the implementation of information technology and mathematical modeling to solve issues concerning environmental sustainability. The examples include using fuzzy weighted multivariate regression to predict the water quality index at Perak River in Malaysia;
Описание: This edited volume assesses capabilities of data mining algorithms for spatial modeling of natural hazards in different countries based on a collection of essays written by experts in the field. The book is organized on different hazards including landslides, flood, forest fire, land subsidence, earthquake, and gully erosion. Chapters were peer-reviewed by recognized scholars in the field of natural hazards research. Each chapter provides an overview on the topic, methods applied, and discusses examples used. The concepts and methods are explained at a level that allows undergraduates to understand and other readers learn through examples. This edited volume is shaped and structured to provide the reader with a comprehensive overview of all covered topics. It serves as a reference for researchers from different fields including land surveying, remote sensing, cartography, GIS, geophysics, geology, natural resources, and geography. It also serves as a guide for researchers, students, organizations, and decision makers active in land use planning and hazard management.
Описание: You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.
Автор: Lech Polkowski; Shusaku Tsumoto; Tsau Y. Lin Название: Rough Set Methods and Applications ISBN: 3662003767 ISBN-13(EAN): 9783662003763 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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