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Statistical Learning and Data Science (Chapman & Hall/CRC Computer Science & Data Analysis)

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Synopses & Reviews

Publisher Comments:

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.

Book News Annotation:

Researchers in statistics, or who use statistics in other fields, present recent findings concerning issues that arise in all the data sciences. In sections on statistical and machine learning, foundations and applications in data science, and complex data, they discuss such topics as mining on social networks, conformal predictors in semi-supervised cases, the art and future prospects of semantics from narrative, the Bayesian analysis of structural equation models using parameter expansion, clustering trajectories of a three-way longitudinal dataset, and the methodological richness of functional data analysis. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

Driven by a vast range of applications, data analysis and learning from data are vibrant areas of research. Various methodologies, including unsupervised data analysis, supervised machine learning, and semisupervised techniques, have continued to develop to cope with the increasing amount of data collected through modern technology. With a focus on applications, this volume presents contributions from some of the leading researchers in the different fields of data analysis. Synthesizing the methodologies into a coherent framework, the book covers a range of topics, from large-scale machine learning to synthesis objects analysis.

Product Details

ISBN:
9781439867631
Author:
Summa, Mireille Gettler
Publisher:
CRC Press
Subject:
Mathematics - Statistics
Publication Date:
20111231
Binding:
HARDCOVER
Language:
English

Related Subjects

Business » General
Business » Management
Business » Writing
Computers and Internet » Computers Reference » General
Science and Mathematics » Biology » General
Science and Mathematics » Mathematics » Computer
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Statistical Learning and Data Science (Chapman & Hall/CRC Computer Science & Data Analysis) New Hardcover
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Product details pages CRC Press - English 9781439867631 Reviews:
"Synopsis" by , Driven by a vast range of applications, data analysis and learning from data are vibrant areas of research. Various methodologies, including unsupervised data analysis, supervised machine learning, and semisupervised techniques, have continued to develop to cope with the increasing amount of data collected through modern technology. With a focus on applications, this volume presents contributions from some of the leading researchers in the different fields of data analysis. Synthesizing the methodologies into a coherent framework, the book covers a range of topics, from large-scale machine learning to synthesis objects analysis.
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