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Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques, Marwala Tshilidzi


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Автор: Marwala Tshilidzi
Название:  Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques
ISBN: 9781605663364
Издательство: Mare Nostrum (Eurospan)
Классификация:
ISBN-10: 1605663360
Обложка/Формат: Hardcover
Страницы: 306
Вес: 1.13 кг.
Дата издания: 02.05.2011
Язык: English
Иллюстрации: Illustrations
Размер: 28.45 x 22.10 x 2.29 cm
Читательская аудитория: Postgraduate, research & scholarly
Подзаголовок: Knowledge optimization techniques
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Поставляется из: Англии
Описание: The issue of missing data imputation has been extensively explored in information engineering. This book presents methods and technologies in estimation of missing values given the observed data. It covers techniques such as radial basis functions, support vector machines, and principal component analysis.


Applied Modeling Techniques and Data Analysis 1: Computational Data Analysis Methods and Tools

Автор: Karagrigoriou Alex, Parpoula Christina, Dimotikalis Yannis
Название: Applied Modeling Techniques and Data Analysis 1: Computational Data Analysis Methods and Tools
ISBN: 1786306735 ISBN-13(EAN): 9781786306739
Издательство: Wiley
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Цена: 21851.00 р.
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Описание:

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen

Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically.

This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

Big data analytics and intelligence

Название: Big data analytics and intelligence
ISBN: 1839091002 ISBN-13(EAN): 9781839091001
Издательство: Emerald
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Цена: 16870.00 р.
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Описание: Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.

Computational Intelligence for Big Data Analysis

Автор: D.P. Acharjya; Satchidananda Dehuri; Sugata Sanyal
Название: Computational Intelligence for Big Data Analysis
ISBN: 3319165976 ISBN-13(EAN): 9783319165974
Издательство: Springer
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Цена: 18284.00 р.
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Описание: The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing.

Advances in Data Analysis with Computational Intelligence Methods

Автор: Adam E Gaw?da; Janusz Kacprzyk; Leszek Rutkowski;
Название: Advances in Data Analysis with Computational Intelligence Methods
ISBN: 3319679457 ISBN-13(EAN): 9783319679457
Издательство: Springer
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Цена: 20962.00 р.
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Описание:

This book is a tribute to Professor Jacek Żurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Żurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few.

At the same time, the book recognizes and honors Professor Żurada's dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world's largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields.

The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.


Optimization Techniques for Problem Solving in Uncertainty

Автор: Surafel Luleseged Tilahun, Jean Medard T. Ngnotchouye
Название: Optimization Techniques for Problem Solving in Uncertainty
ISBN: 1522550917 ISBN-13(EAN): 9781522550914
Издательство: Mare Nostrum (Eurospan)
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Цена: 28413.00 р.
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Описание: When it comes to optimization techniques, in some cases, the available information from real models may not be enough to construct either a probability distribution or a membership function for problem solving. In such cases, there are various theories that can be used to quantify the uncertain aspects.Optimization Techniques for Problem Solving in Uncertainty is a scholarly reference resource that looks at uncertain aspects involved in different disciplines and applications. Featuring coverage on a wide range of topics including uncertain preference, fuzzy multilevel programming, and metaheuristic applications, this book is geared towards engineers, managers, researchers, and post-graduate students seeking emerging research in the field of optimization.


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