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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.
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.
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