Statistical learning and data science: "A Chapman & Hall book."
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla.
CRC Press
2012
|
| Schriftenreihe: | Series in computer science and data analysis
|
| Schlagworte: | |
| Beschreibung: | Includes bibliographical references "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. "-- |
| Beschreibung: | 1 Online-Ressource (xv, 223 p.) |
| ISBN: | 9781439867648 |
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| discipline | Informatik Mathematik |
| format | Electronic eBook |
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| id | DE-604.BV040068719 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:16:44Z |
| institution | BVB |
| isbn | 9781439867648 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024925396 |
| oclc_num | 773034863 |
| open_access_boolean | |
| physical | 1 Online-Ressource (xv, 223 p.) |
| psigel | ZDB-38-EBR |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Series in computer science and data analysis |
| spelling | Statistical learning and data science "A Chapman & Hall book." edited by Mireille Gettler Summa ... [et al.] Boca Raton, Fla. CRC Press 2012 1 Online-Ressource (xv, 223 p.) txt rdacontent c rdamedia cr rdacarrier Series in computer science and data analysis Includes bibliographical references "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. "-- Online-Ausgabe Palo Alto, Calif. ebrary605 L Online-Ausgabe Machine learning / Statistical methods Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Summa, Mireille Gettler Sonstige oth Reproduktion von Statistical learning and data science 2012 Erscheint auch als Druck-Ausgabe, Hardcover 978-1-4398-6763-1 |
| spellingShingle | Statistical learning and data science "A Chapman & Hall book." Machine learning / Statistical methods Data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Statistical learning and data science "A Chapman & Hall book." |
| title_auth | Statistical learning and data science "A Chapman & Hall book." |
| title_exact_search | Statistical learning and data science "A Chapman & Hall book." |
| title_full | Statistical learning and data science "A Chapman & Hall book." edited by Mireille Gettler Summa ... [et al.] |
| title_fullStr | Statistical learning and data science "A Chapman & Hall book." edited by Mireille Gettler Summa ... [et al.] |
| title_full_unstemmed | Statistical learning and data science "A Chapman & Hall book." edited by Mireille Gettler Summa ... [et al.] |
| title_short | Statistical learning and data science |
| title_sort | statistical learning and data science a chapman hall book |
| title_sub | "A Chapman & Hall book." |
| topic | Machine learning / Statistical methods Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Machine learning / Statistical methods Data mining Data Mining |
| work_keys_str_mv | AT summamireillegettler statisticallearninganddatascienceachapmanhallbook |