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Robust Representation for Data Analytics, Sheng Li; Yun Fu


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Цена: 111790.00T
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Автор: Sheng Li; Yun Fu
Название:  Robust Representation for Data Analytics
ISBN: 9783319867960
Издательство: Springer
Классификация:



ISBN-10: 3319867962
Обложка/Формат: Soft cover
Страницы: 224
Вес: 0.50 кг.
Дата издания: 2017
Серия: Advanced Information and Knowledge Processing
Язык: English
Издание: Softcover reprint of
Иллюстрации: 49 illustrations, color; 3 illustrations, black and white; xi, 224 p. 52 illus., 49 illus. in color.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Подзаголовок: Models and Applications
Ссылка на Издательство: Link
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Поставляется из: Германии

Cloud Analytics for Industry 4.0

Автор: Gouse Baig Mohammad, S. Shitharth, Sachi Nandan Mohanty, Sirisha Potluri
Название: Cloud Analytics for Industry 4.0
ISBN: 3110771497 ISBN-13(EAN): 9783110771497
Издательство: Walter de Gruyter
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Цена: 192090.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides research on the state-of-the-art methods for data management in the fourth industrial revolution, with particular focus on cloud.based data analytics for digital manufacturing infrastructures. Innovative techniques and methods for secure, flexible and profi table cloud manufacturing will be gathered to present advanced and specialized research in the selected area.


Fundamentals of Stream Processing

Автор: Andrade
Название: Fundamentals of Stream Processing
ISBN: 1107015545 ISBN-13(EAN): 9781107015548
Издательство: Cambridge Academ
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Цена: 91870.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book teaches fundamentals of the stream processing paradigm that addresses performance, scalability and usability challenges in extracting insights from massive amounts of live, streaming data. It presents core principles behind application design, system infrastructure and analytics, coupled with real-world examples for a comprehensive understanding of the stream processing area.

Sport Business Analytics

Автор: C. Keith Harrison, Scott Bukstein
Название: Sport Business Analytics
ISBN: 1498761267 ISBN-13(EAN): 9781498761260
Издательство: Taylor&Francis
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Цена: 117390.00 T
Наличие на складе: Нет в наличии.
Описание:

Developing and implementing a systematic analytics strategy can result in a sustainable competitive advantage within the sport business industry. This timely and relevant book provides practical strategies to collect data and then convert that data into meaningful, value-added information and actionable insights. Its primary objective is to help sport business organizations utilize data-driven decision-making to generate optimal revenue from such areas as ticket sales and corporate partnerships. To that end, the book includes in-depth case studies from such leading sports organizations as the Orlando Magic, Tampa Bay Buccaneers, Duke University, and the Aspire Group.

The core purpose of sport business analytics is to convert raw data into information that enables sport business professionals to make strategic business decisions that result in improved company financial performance and a measurable and sustainable competitive advantage. Readers will learn about the role of big data and analytics in:

  • Ticket pricing
  • Season ticket member retention
  • Fan engagement
  • Sponsorship valuation
  • Customer relationship management
  • Digital marketing
  • Market research
  • Data visualization.

This book examines changes in the ticketing marketplace and spotlights innovative ticketing strategies used in various sport organizations. It shows how to engage fans with social media and digital analytics, presents techniques to analyze engagement and marketing strategies, and explains how to utilize analytics to leverage fan engagement to enhance revenue for sport organizations.

Filled with insightful case studies, this book benefits both sports business professionals and students. The concluding chapter on teaching sport analytics further enhances its value to academics.

Learning Representation for Multi-View Data Analysis

Автор: Zhengming Ding; Handong Zhao; Yun Fu
Название: Learning Representation for Multi-View Data Analysis
ISBN: 3030007332 ISBN-13(EAN): 9783030007331
Издательство: Springer
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Цена: 111790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal.A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Автор: Sujatha R., Aarthy S. L., Vettriselvan R.
Название: Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics
ISBN: 0367466635 ISBN-13(EAN): 9780367466633
Издательство: Taylor&Francis
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Цена: 117390.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data science revolves around two giants, which are big data analytics and deep learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of big data along with deep learning systems.

Multimedia Ontology

Автор: Chaudhury, Santanu , Mallik, Anupama , Ghosh, Hi
Название: Multimedia Ontology
ISBN: 0367445824 ISBN-13(EAN): 9780367445829
Издательство: Taylor&Francis
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Цена: 63280.00 T
Наличие на складе: Нет в наличии.
Описание:

The result of more than 15 years of collective research, Multimedia Ontology: Representation and Applications provides a theoretical foundation for understanding the nature of media data and the principles involved in its interpretation. The book presents a unified approach to recent advances in multimedia and explains how a multimedia ontology can fill the semantic gap between concepts and the media world. It relays real-life examples of implementations in different domains to illustrate how this gap can be filled.

The book contains information that helps with building semantic, content-based search and retrieval engines and also with developing vertical application-specific search applications. It guides you in designing multimedia tools that aid in logical and conceptual organization of large amounts of multimedia data. As a practical demonstration, it showcases multimedia applications in cultural heritage preservation efforts and the creation of virtual museums.

The book describes the limitations of existing ontology techniques in semantic multimedia data processing, as well as some open problems in the representations and applications of multimedia ontology. As an antidote, it introduces new ontology representation and reasoning schemes that overcome these limitations. The long, compiled efforts reflected in Multimedia Ontology: Representation and Applications are a signpost for new achievements and developments in efficiency and accessibility in the field.


Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Автор: Qian Wang; Fausto Milletari; Hien V. Nguyen; Shadi
Название: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data
ISBN: 3030333906 ISBN-13(EAN): 9783030333904
Издательство: Springer
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Цена: 46570.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

Digital Holographic Data Representation and Compression

Автор: Yafei Xing
Название: Digital Holographic Data Representation and Compression
ISBN: 0128028548 ISBN-13(EAN): 9780128028544
Издательство: Elsevier Science
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Цена: 61760.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With the increasing interest in holography for 3D imaging applications, there is a need to develop and use hologram compression techniques for the efficient storage and transmission of holographic data. This book gives a broad overview of the state-of-the-art techniques for the efficient compression and representation of digital holographic data, addressing both still and moving data sequences.

Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Автор: Stanis?aw Kozielski; Dariusz Mrozek; Pawe? Kasprow
Название: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation
ISBN: 3319582739 ISBN-13(EAN): 9783319582733
Издательство: Springer
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Цена: 83850.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 13th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2017, held in Ustron, Poland, in May/June 2017.It consists of 44 carefully reviewed papers selected from 118 submissions.

A knowledge representation practionary

Автор: Bergman, Michael K.
Название: A knowledge representation practionary
ISBN: 3319980912 ISBN-13(EAN): 9783319980911
Издательство: Springer
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Цена: 186330.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

This major work on knowledge representation is based on the writings of Charles S. Peirce, a logician, scientist, and philosopher of the first rank at the beginning of the 20th century. This book follows Peirce's practical guidelines and universal categories in a structured approach to knowledge representation that captures differences in events, entities, relations, attributes, types, and concepts. Besides the ability to capture meaning and context, the Peircean approach is also well-suited to machine learning and knowledge-based artificial intelligence. Peirce is a founder of pragmatism, the uniquely American philosophy.

Knowledge representation is shorthand for how to represent human symbolic information and knowledge to computers to solve complex questions. KR applications range from semantic technologies and knowledge management and machine learning to information integration, data interoperability, and natural language understanding. Knowledge representation is an essential foundation for knowledge-based AI.

This book is structured into five parts. The first and last parts are bookends that first set the context and background and conclude with practical applications. The three main parts that are the meat of the approach first address the terminologies and grammar of knowledge representation, then building blocks for KR systems, and then design, build, test, and best practices in putting a system together. Throughout, the book refers to and leverages the open source KBpedia knowledge graph and its public knowledge bases, including Wikipedia and Wikidata. KBpedia is a ready baseline for users to bridge from and expand for their own domain needs and applications. It is built from the ground up to reflect Peircean principles.

This book is one of timeless, practical guidelines for how to think about KR and to design knowledge management (KM) systems. The book is grounded bedrock for enterprise information and knowledge managers who are contemplating a new knowledge initiative.

This book is an essential addition to theory and practice for KR and semantic technology and AI researchers and practitioners, who will benefit from Peirce's profound understanding of meaning and context.


Graph Representation Learning

Автор: Hamilton William L.
Название: Graph Representation Learning
ISBN: 1681739658 ISBN-13(EAN): 9781681739656
Издательство: Mare Nostrum (Eurospan)
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Цена: 75770.00 T
Наличие на складе: Невозможна поставка.
Описание:

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.

This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs--a nascent but quickly growing subset of graph representation learning.


Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database

Автор: Jones Herbert
Название: Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database
ISBN: 1647483263 ISBN-13(EAN): 9781647483265
Издательство: Неизвестно
Рейтинг:
Цена: 27580.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data science has a huge impact on how companies conduct business, and those who don`t learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.


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