Numeric Computation and Statistical Data Analysis on the Java Platform [recurso electrónico] / by Sergei V. Chekanov.

Por: Chekanov, Sergei V [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Advanced Information and Knowledge ProcessingEditor: Cham : Springer International Publishing : Imprint: Springer, 2016Descripción: XXVI, 620 p. 92 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319285313Tema(s): Computer science | Programming languages (Electronic computers) | Data mining | Computer Science | Programming Languages, Compilers, Interpreters | Data Mining and Knowledge DiscoveryFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 005.13 Clasificación LoC:QA76.7-76.73QA76.76.C65Recursos en línea: Libro electrónicoTexto
Contenidos:
Java Computational Platform -- Introduction to Jython -- Mathematical Functions -- Data Arrays -- Linear Algebra and Equations -- Symbolic Computations -- Histograms -- Scientific Visualization -- File Input and Output -- Probability and Statistics -- Linear Regression and Curve Fitting -- Data Analysis and Data Mining -- Neural Networks -- Finding Regularities and Data Classification -- Miscellaneous Topics -- Using Other Languages on the Java Platform -- Octave-style Scripting Using Java -- Index -- Index of Code Examples.
En: Springer eBooksResumen: Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.
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Java Computational Platform -- Introduction to Jython -- Mathematical Functions -- Data Arrays -- Linear Algebra and Equations -- Symbolic Computations -- Histograms -- Scientific Visualization -- File Input and Output -- Probability and Statistics -- Linear Regression and Curve Fitting -- Data Analysis and Data Mining -- Neural Networks -- Finding Regularities and Data Classification -- Miscellaneous Topics -- Using Other Languages on the Java Platform -- Octave-style Scripting Using Java -- Index -- Index of Code Examples.

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.

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