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Guide to intelligent data analysis : how to intelligently make sense of real data / Michael R. Berthold ... [et al.].

Contributor(s): Berthold, M. (Michael).
Material type: materialTypeLabelBook; Format: print Series: Texts in computer science.Publisher: London : Springer, c2010Description: xiii, 394 p. : il. ; 24 cm.ISBN: 9781848822597 (alk. paper); 1848822596 (alk. paper).Subject(s): Mathematical statistics | Mathematical statistics -- Data processing | Artificial intelligence | Inteligencia artificial | Estadística matemática | Estadística matemática -- Proceso de datos
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Monografías 03. BIBLIOTECA INGENIERÍA PUERTO REAL
681.3/GUI/ (Browse shelf) Checked out BIBLIOG. RECOM. 29/01/2021 3741243198
Manuales (7 días) 03. BIBLIOTECA INGENIERÍA PUERTO REAL
681.3/GUI (Browse shelf) Available   Shelving location | Bibliomaps® BIBLIOG. RECOM. 3744057221

APRENDIZAJE COMPUTACIONAL GRADO EN INGENIERÍA INFORMÁTICA

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Bibliografía: (p. 383). - índices

Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now {u0874} least in principle {u0CEF}lve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of {u4CAF}wning in information, but starving for knowledgeö the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following ones̕ exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany." Presents

Índice: Introduction.- Practical Data Analysis: An Example.- Project Understanding.- Data Understanding.- Principles of Modeling.- Data Preparation.- Finding Patterns.- Finding Explanations.- Finding Predictors.- Evaluation and Deployment.- Appendix A: Statistics.- Appendix B: The R Project.- Appendix C: KNIME.

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