Isbn: 9789811396632 - predictive data mining models (10 risultati)

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    • Lingua: Inglese

      Editore: Springer, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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    • Lingua: Inglese

      Editore: Springer, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Condizione: New. 2nd ed. 2020 edition NO-PA16APR2015-KAP.

    • Lingua: Inglese

      Editore: Springer, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books

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      Hardcover. Condizione: Brand New. 2nd edition. 125 pages. 9.25x6.25x0.50 inches. In Stock.

    • Lingua: Inglese

      Editore: Springer, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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      Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

    • Lingua: Inglese

      Editore: Springer, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand

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      EUR 94,25

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      Condizione: new. Questo è un articolo print on demand.

    • Lingua: Inglese

      Editore: Springer Nature Singapore Aug 2019, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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      Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links. 140 pp. Englisch.

    • Lingua: Inglese

      Editore: Springer Singapore, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: moluna, Greven, Germaniamoluna

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      EUR 101,04

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      Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a comprehensive overview of knowledge management, big data, and basic descriptive data mining methods and softwareIllustrates concepts with typical dataDemonstrates readily available open source software.

    • Lingua: Inglese

      Editore: Springer, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: Majestic Books, Hounslow, Regno UnitoMajestic Books

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    • Lingua: Inglese

      Editore: Springer, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios

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      EUR 164,56

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    • Lingua: Inglese

      Editore: Springer, Springer Aug 2019, 2019

      9811396639 / 9789811396632

      Serie: Libro 9 di 10 - Computational Risk Management

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      Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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      EUR 117,69

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      Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 140 pp. Englisch.