Описание: Written for a wide audience of biologists and philosophers, this text gives an excellent overview of the species problem without delving into the many feuds between the different schools of taxonomy. A perfect companion for field taxonomists and students dealing with the problem.
Описание: This is the first book to provide a comprehensive and systematic introduction to the ranking methods for interval-valued intuitionistic fuzzy sets, multi-criteria decision-making methods with interval-valued intuitionistic fuzzy sets, and group decision-making methods with interval-valued intuitionistic fuzzy preference relations.
Описание: These are the proceedings from the International Workshop on Penicillium and Aspergillus. The theme was the systematics of these fungi. The taxonomy has developed from morphological concepts, and these papers consider the integration of molecular, physiological and biochemical methods.
Автор: Abe, Shigeo Название: Pattern classification ISBN: 1447110773 ISBN-13(EAN): 9781447110774 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers.
Автор: Urszula Bentkowska Название: Interval-Valued Methods in Classifications and Decisions ISBN: 3030129268 ISBN-13(EAN): 9783030129262 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes novel algorithms based on interval-valued fuzzy methods that are expected to improve classification and decision-making processes under incomplete or imprecise information. At first, it introduces interval-valued fuzzy sets. It then discusses new methods for aggregation on interval-valued settings, and the most common properties of interval-valued aggregation operators. It then presents applications such as decision making using interval-valued aggregation, and classification in case of missing values. Interesting applications of the developed algorithms to DNA microarray analysis and in medical decision support systems are shown. The book is intended not only as a timely report for the community working on fuzzy sets and their extensions but also for researchers and practitioners dealing with the problems of uncertain or imperfect information.
Описание: Discover how categorisation has shaped our view of the natural world with How Zoologists Organize Things. The book unveils wild truths and even wilder myths about animals, as perpetuated by zoologists - revealing how much more there is to learn, and unlearn.
Описание: This book presents machine learning models and algorithms to address big data classification problems. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The third part presents the topics required to understand and select machine learning techniques to classify big data.
Описание: This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain-computer interfaces.
Описание: When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame.
Автор: Barbara P?kala Название: Uncertainty Data in Interval-Valued Fuzzy Set Theory ISBN: 3030067432 ISBN-13(EAN): 9783030067434 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Поставка под заказ.
Описание: This book offers an introduction to fuzzy sets theory and their operations, with a special focus on aggregation and negation functions. Particular attention is given to interval-valued fuzzy sets and Atanassov’s intuitionistic fuzzy sets and their use in uncertainty models involving imperfect or unknown information. The theory and application of interval-values fuzzy sets to various decision making problems represent the central core of this book, which describes in detail aggregation operators and their use with imprecise data represented as intervals. Interval-valued fuzzy relations, compatibility measures of interval and the transitivity property are thoroughly covered. With its good balance between theoretical considerations and applications of originally developed algorithms to real-world problem, the book offers a timely, inspiring guide to mathematicians and engineers developing new decision making models or implementing/applying existing ones to a wide range of applications involving imprecise or incomplete data.
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