Автор: Leskovec Jure Название: Mining of Massive Datasets ISBN: 1108476341 ISBN-13(EAN): 9781108476348 Издательство: Cambridge Academ Рейтинг: Цена: 10771.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Автор: Morzy Tadeusz; Patrick Valduriez; Ladjel Bellatrec Название: Advances in Databases and Information Systems ISBN: 3319231340 ISBN-13(EAN): 9783319231341 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the thoroughly refereed proceedings of the 19th East European Conference on Advances in Databases and Information Systems, ADBIS 2015, held in Poitiers, France, in September 2015. The 31 full papers and 18 short papers presented were carefully selected and reviewed from 135 submissions.
Описание: This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.
Описание: This book describes how to use computational intelligence and artificial intelligence tools to improve the decision-making process in new product development.
Автор: Matthias Dehmer, Salissou Moutari, Frank Emmert-Streib Название: Mathematical Foundations of Data Science Using R ISBN: 311056467X ISBN-13(EAN): 9783110564679 Издательство: Walter de Gruyter Цена: 9288.00 р. Наличие на складе: Нет в наличии.
Описание:
In order best exploit the incredible quantities of data being generated in most diverse disciplines data sciences increasingly gain worldwide importance. The book gives the mathematical foundations to handle data properly. It introduces basics and functionalities of the R programming language which has become the indispensable tool for data sciences. Thus it delivers the reader the skills needed to build own tool kits of a modern data scientist.
Описание: Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance.
The Handbook of Research on Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.
Автор: Pennington Diane Название: Social Tagging for Linking Data Across Environments ISBN: 1783303387 ISBN-13(EAN): 9781783303380 Издательство: Facet Рейтинг: Цена: 16368.00 р. Наличие на складе: Нет в наличии.
Описание: This book, representing researchers and practitioners across different information professions, will explore how social tags can link content across a variety of environments.
Автор: Christophe Claramunt; Markus Schneider; Raymond Ch Название: Advances in Spatial and Temporal Databases ISBN: 3319223623 ISBN-13(EAN): 9783319223629 Издательство: Springer Рейтинг: Цена: 10062.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 14th International Symposium on Spatial and Temporal Databases, SSTD 2015, held in Hong Kong, China, in August 2015. The 24 revised full papers together with 8 demos presented were carefully reviewed and selected from 64 submissions.
Автор: Karl Aberer; Vana Kalogeraki; Manolis Koubarakis Название: Databases, Information Systems, and Peer-to-Peer Computing ISBN: 364205899X ISBN-13(EAN): 9783642058998 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Aastha Madaan; Shinji Kikuchi; Subhash Bhalla Название: Databases in Networked Information Systems ISBN: 3642371337 ISBN-13(EAN): 9783642371332 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 8th International Workshop on Databases in Networked Information Systems, DNIS 2013, held in Aizu-Wakamatsu, Japan in March 2013. The workshop generally puts the main focus on data semantics and infrastructure for information management and interchange.
Описание: Showcases an effective methodology for classification and clustering of web sites from a usability point of view. While the clustering and classification is accomplished by using an open source tool, WEKA, the basic dataset for the selected websites has been arrived at by using a free tool site-analyser. As a case study, several commercial websites are analysed.
Автор: Anandakumar Haldorai, Arulmurugan Ramu Название: Big Data Analytics for Sustainable Computing ISBN: 1522597506 ISBN-13(EAN): 9781522597506 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 32987.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science.
Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.
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