Challenges and Applications of Data Analytics in Social Perspectives, V. Sathiyamoorthi, Atilla Elci
Автор: V. Sathiyamoorthi, Atilla Elci Название: Challenges and Applications of Data Analytics in Social Perspectives ISBN: 1799825663 ISBN-13(EAN): 9781799825661 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 32987.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. Topics examined include collaborative filtering, data visualization, and edge computing.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Anandakumar Haldorai, Arulmurugan Ramu Название: Cognitive Social Mining Applications in Data Analytics and Forensics ISBN: 1522575227 ISBN-13(EAN): 9781522575221 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data.Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.
Описание: This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification, generalized eigenvalue decomposition and in the optimization of deep neural networks. The monograph focuses on tensor train (TT) and Hierarchical Tucker (HT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide scalable solutions for a variety of otherwise intractable large-scale optimization problems. Tensor Networks for Dimensionality Reduction and Large-scale Optimization Parts 1 and 2 can be used as stand-alone texts, or together as a comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions. See also: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. ISBN 978-1-68083-222-8
Описание: This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
Автор: Ranise Название: Data and Applications Security and Privacy XXX ISBN: 3319414828 ISBN-13(EAN): 9783319414829 Издательство: Springer Рейтинг: Цена: 10482.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 30th Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2016, held in trento, Itlay, in July 2016.
Автор: Giovanni Livraga; Sencun Zhu Название: Data and Applications Security and Privacy XXXI ISBN: 3319611755 ISBN-13(EAN): 9783319611754 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 31st Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2017, held in Philadelphia, PA, USA, in July 2017. The 21 full papers and 9 short papers presented were carefully reviewed and selected from 59 submissions.
Автор: Kerschbaum Название: Data and Applications Security and Privacy XXXII ISBN: 3319957287 ISBN-13(EAN): 9783319957289 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 32nd Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2018, held in Bergamo, Italy, in July 2018. The 16 full papers and 5 short papers presented were carefully reviewed and selected from 50 submissions.
Автор: Kaya Название: Social Network Based Big Data Analysis and Applications ISBN: 3319781952 ISBN-13(EAN): 9783319781952 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a timely collection of chapters that present the state of the art within the analysis and application of big data.
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