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Statistical learning and data science /

"Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached ne...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Summa, Mireille Gettler (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton : CRC Press, Taylor & Francis Group, [2012]
Colección:Series in computer science and data analysis.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 0 0 |a Statistical learning and data science /  |c edited by Mireille Gettler Summa, Léon Bottou, Bernard Goldfarb, Fionn Murtagh, Catherine Pardoux, and Myriam Touati. 
264 1 |a Boca Raton :  |b CRC Press, Taylor & Francis Group,  |c [2012] 
264 4 |c ©2012 
300 |a 1 online resource (xv, 227 pages) :  |b illustrations 
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490 1 |a Chapman & Hall/CRC computer science & data analysis 
500 |a "A Chapman & Hall Book." 
504 |a Includes bibliographical references and index. 
505 0 |a I. Statistical and machine learning -- II. Data science, foundations, and applications -- III. Complex data. 
520 |a "Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit. Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different fields of data analysis to the major theoretical results in the domain. On the methodological front, the volume includes conformal prediction and frameworks for assessing confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data. Over the history of multidimensional data analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and unsupervised data analysis, provide great capability for addressing the digital data deluge. Exploring the foundations and recent breakthroughs in the field, Statistical Learning and Data Science demonstrates how data analysis can improve personal and collective health and the well-being of our social, business, and physical environments"--Provided by publisher. 
588 0 |a Print version record. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Machine learning  |x Statistical methods. 
650 0 |a Data mining. 
650 6 |a Apprentissage automatique  |x Méthodes statistiques. 
650 6 |a Exploration de données (Informatique) 
650 7 |a BUSINESS & ECONOMICS  |x Statistics.  |2 bisacsh 
650 7 |a COMPUTERS  |x Enterprise Applications  |x Business Intelligence Tools.  |2 bisacsh 
650 7 |a COMPUTERS  |x Intelligence (AI) & Semantics.  |2 bisacsh 
650 7 |a COMPUTERS  |x Machine Theory.  |2 bisacsh 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
650 7 |a Data mining.  |2 fast  |0 (OCoLC)fst00887946 
650 7 |a Machine learning  |x Statistical methods.  |2 fast  |0 (OCoLC)fst01004801 
700 1 |a Summa, Mireille Gettler,  |e editor. 
776 0 8 |i Print version:  |t Statistical learning and data science.  |d Boca Raton, FL : CRC Press, ©2012  |z 9781439867631  |w (DLC) 2011036558  |w (OCoLC)703209011 
830 0 |a Series in computer science and data analysis. 
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