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Rough Set–Based Classification Systems, Robert K. Nowicki


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Цена: 13974.00р.
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Автор: Robert K. Nowicki
Название:  Rough Set–Based Classification Systems
ISBN: 9783030038946
Издательство: Springer
Классификация:


ISBN-10: 3030038947
Обложка/Формат: Hardcover
Страницы: 188
Вес: 0.48 кг.
Дата издания: 2019
Серия: Studies in Computational Intelligence
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 125 illustrations, black and white; xiii, 188 p. 125 illus.
Размер: 234 x 156 x 13
Читательская аудитория: Professional & vocational
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
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.

Дополнительное описание: Introduction.- Rough Set Theory Fundamentals.- Rough Fuzzy Classi?cation Systems.- Fuzzy Rough Classi?cation Systems.- Rough Neural Network Classi?er.- Rough Nearest Neighbour Classi?er.- Ensembles of Rough Set–Based Classi?ers.- Final Remarks.



EEG Signal Analysis and Classification

Автор: Siuly Siuly; Yan Li; Yanchun Zhang
Название: EEG Signal Analysis and Classification
ISBN: 3319476521 ISBN-13(EAN): 9783319476520
Издательство: Springer
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Цена: 19564.00 р.
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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.

Classification theory for abstract elementary classes

Автор: Shelah, Saharon
Название: Classification theory for abstract elementary classes
ISBN: 1904987710 ISBN-13(EAN): 9781904987710
Издательство: Неизвестно
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Цена: 7449.00 р.
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Описание: An abstract elementary class is a class of structures of the same vocabulary (like a class of rings, or a class of fields), with a partial order that generalizes the relation "A is a substructure (or an elementary substructure) of B". The requirements are that the class is closed under isomorphism, and that isomorphic structures have isomorphic (generalized) substructures; we also require that our classes share some of the most basic properties of elementary classes, like closure under unions of increasing chains of substructures. We would like to classify this general family; in the sense of proving dichotomies: either we can understand the structure of all models in our class or there are many to some extent. More specifically we would like to generalize the theory about categoricity and superstability to this context.

New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic

Автор: Amezcua
Название: New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic
ISBN: 3319737724 ISBN-13(EAN): 9783319737720
Издательство: Springer
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Цена: 6986.00 р.
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Описание: In this book a new model for data classification was developed. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil.

Classification techniques for medical image analysis and computer aided diagnosis /

Автор: Dey, Nilanjan.
Название: Classification techniques for medical image analysis and computer aided diagnosis /
ISBN: 0128180048 ISBN-13(EAN): 9780128180044
Издательство: Elsevier Science
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Цена: 19875.00 р.
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Описание:

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images.

  • Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges
  • Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications
  • Introduces several techniques for medical image processing and analysis for CAD systems design
Classification theory for abstract elementary classes

Автор: Shelah, Saharon
Название: Classification theory for abstract elementary classes
ISBN: 1904987729 ISBN-13(EAN): 9781904987727
Издательство: Неизвестно
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Цена: 7449.00 р.
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Classification and Clustering for Knowledge Discovery

Автор: Saman K. Halgamuge; Lipo Wang
Название: Classification and Clustering for Knowledge Discovery
ISBN: 3642065422 ISBN-13(EAN): 9783642065422
Издательство: Springer
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Цена: 29209.00 р.
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Описание: This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees.

Inductive Inference for Large Scale Text Classification

Автор: Catarina Silva; Bernadete Ribeiro
Название: Inductive Inference for Large Scale Text Classification
ISBN: 3642261345 ISBN-13(EAN): 9783642261343
Издательство: Springer
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Цена: 16977.00 р.
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Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.

Inductive Inference for Large Scale Text Classification

Автор: Catarina Silva; Bernadete Ribeiro
Название: Inductive Inference for Large Scale Text Classification
ISBN: 3642045324 ISBN-13(EAN): 9783642045325
Издательство: Springer
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Цена: 20896.00 р.
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Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.

Prediction and Classification of Respiratory Motion

Автор: Suk Jin Lee; Yuichi Motai
Название: Prediction and Classification of Respiratory Motion
ISBN: 3642415083 ISBN-13(EAN): 9783642415081
Издательство: Springer
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Цена: 18284.00 р.
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Описание: This book examines current radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. The proposed method improves treatments by considering breathing pattern for accurate dose calculation.

Biological Signals Classification and Analysis

Автор: Kamran Kiasaleh
Название: Biological Signals Classification and Analysis
ISBN: 3642548784 ISBN-13(EAN): 9783642548789
Издательство: Springer
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Цена: 23508.00 р.
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Описание: Unlike wireless communication systems, biological entities produce signals with underlying nonlinear, chaotic nature that elude classification using the standard signal processing techniques, which have been developed over the past several decades for dealing primarily with standard communication systems.

Pattern Classification of Medical Images: Computer Aided Diagnosis

Автор: Xiao-Xia Yin; Sillas Hadjiloucas; Yanchun Zhang
Название: Pattern Classification of Medical Images: Computer Aided Diagnosis
ISBN: 3319570269 ISBN-13(EAN): 9783319570266
Издательство: Springer
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Цена: 16769.00 р.
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Описание: This book presents advances in biomedical imaging analysis and processing techniques using time dependent medical image datasets for computer aided diagnosis.

Multilabel Classification

Автор: Herrera
Название: Multilabel Classification
ISBN: 3319411101 ISBN-13(EAN): 9783319411101
Издательство: Springer
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Цена: 18167.00 р.
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Описание:

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are:
• The special characteristics of multi-labeled data and the metrics available to measure them.
• The importance of taking advantage of label correlations to improve the results.
• The different approaches followed to face multi-label classification.
• The preprocessing techniques applicable to multi-label datasets.
• The available software tools to work with multi-label data.
This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

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