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Author: Kaempgen, Benedikt
Publisher: KIT Scientific Publishing
ISBN: 3731503794
Category : Data structures (Computer science)
Languages : en
Pages : 286
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Book Description
If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches.
Author: Kaempgen, Benedikt
Publisher: KIT Scientific Publishing
ISBN: 3731503794
Category : Data structures (Computer science)
Languages : en
Pages : 286
Get Book Here
Book Description
If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches.
Author: Klinger, Jochen
Publisher: KIT Scientific Publishing
ISBN: 373150393X
Category :
Languages : en
Pages : 448
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Book Description
Author: Johannes De Smedt
Publisher: Springer Nature
ISBN: 3031561074
Category :
Languages : en
Pages : 534
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Book Description
Author: Gergely Daroczi
Publisher: Packt Publishing Ltd
ISBN: 1783982039
Category : Computers
Languages : en
Pages : 397
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Book Description
Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.
Author: Chiara Romualdi
Publisher: Frontiers Media SA
ISBN: 2889661512
Category : Medical
Languages : en
Pages : 187
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Book Description
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Author: Thorsten Joachims
Publisher: Springer Science & Business Media
ISBN: 1461509076
Category : Computers
Languages : en
Pages : 218
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Book Description
Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
Author:
Publisher: Elsevier
ISBN: 012805154X
Category : Medical
Languages : en
Pages : 2854
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Book Description
Encyclopedia of Cardiovascular Research and Medicine, Four Volume Set offers researchers over 200 articles covering every aspect of cardiovascular research and medicine, including fully annotated figures, abundant color illustrations and links to supplementary datasets and references. With contributions from top experts in the field, this book is the most reputable and easily searchable resource of cardiovascular-focused basic and translational content for students, researchers, clinicians and teaching faculty across the biomedical and medical sciences. The panel of authors chosen from an international board of leading scholars renders the text trustworthy, contemporary and representative of the global scientific expertise in these domains. The book's thematic structuring of sections and in-depth breakdown of topics encourages user-friendly, easily searchable chapters. Cross-references to related articles and links to further reading and references will further guide readers to a full understanding of the topics under discussion. Readers will find an unparalleled, one-stop resource exploring all major aspects of cardiovascular research and medicine. Presents comprehensive coverage of every aspect of cardiovascular medicine and research Offers readers a broad, interdisciplinary overview of the concepts in cardiovascular research and medicine with applications across biomedical research Includes reputable, foundational content on genetics, cancer, immunology, cell biology and molecular biology Provides a multi-media enriched color-illustrated text with high quality images, graphs and tables.
Author: Shashi Shekhar
Publisher: Springer Science & Business Media
ISBN: 038730858X
Category : Computers
Languages : en
Pages : 1392
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Book Description
The Encyclopedia of GIS provides a comprehensive and authoritative guide, contributed by experts and peer-reviewed for accuracy, and alphabetically arranged for convenient access. The entries explain key software and processes used by geographers and computational scientists. Major overviews are provided for nearly 200 topics: Geoinformatics, Spatial Cognition, and Location-Based Services and more. Shorter entries define specific terms and concepts. The reference will be published as a print volume with abundant black and white art, and simultaneously as an XML online reference with hyperlinked citations, cross-references, four-color art, links to web-based maps, and other interactive features.
Author: Mónica Hebe Vazquez-Levin
Publisher: Frontiers Media SA
ISBN: 2832521800
Category : Medical
Languages : en
Pages : 112
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Book Description
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309287812
Category : Mathematics
Languages : en
Pages : 191
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Book Description
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
