Interval-Valued Methods in Classifications and Decisions, Urszula Bentkowska
Автор: Siuly Siuly; Yan Li; Yanchun Zhang Название: EEG Signal Analysis and Classification ISBN: 3319476521 ISBN-13(EAN): 9783319476520 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems.
Автор: Anil K. Jain Название: Real-Time Object Measurement and Classification ISBN: 3642833276 ISBN-13(EAN): 9783642833274 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Proceedings of the NATO Advanced Research Workshop on Real- Time Object and Environment Measurement and Qualification, held in Maratea, Italy, August 31 - September 3, 1987
Описание: Many species of penicillium and aspergillus are key in biotechnology, food, medicine, biodeterioration and other applied fields, so a practical and stable taxonomy is of vital importance. Developments in science and technology mean that taxonomic classification is no longer confined to classical morphological concepts, and the integration of molecular, physiological and biochemical methods now plays an important role in understanding the classification of these fungi. International experts contribute their expertise, providing value to researchers and professionals in mycology, biotechnology, medicine and regulatory agencies interested in the identification of these fungi.
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.
Описание: This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
Автор: Robert K. Nowicki Название: Rough Set–Based Classification Systems ISBN: 3030038947 ISBN-13(EAN): 9783030038946 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak.
The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.
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.
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.
Автор: Hoenigswald Henry M., Wiener Linda F. Название: Biological Metaphor and Cladistic Classification ISBN: 0812280148 ISBN-13(EAN): 9780812280142 Издательство: Marston Book Services Цена: 13959.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The dynamic aspect of biological systems--the birth, growth, and death of individual organisms, the evolution of one form into another over time--has formed the basis for metaphors used in many fields for both artistic and heuristic purposes. Cladistic classification uses a tree whose branch points are based on the possession of derived or relatively recent characteristics, rather than primitive ones.
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: 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.
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