From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence (Intelligent Systems Reference Library, 109)
Zielesny, Achim
ISBN 10: 3319325442 ISBN 13: 9783319325446
Editorial: Springer, 2016
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Descripción
Descripción:
In. N° de ref. del artículo ria9783319325446_new
Sinopsis:
This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.
The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.
All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.
The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results.
All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code". Leslie A. Piegl (Review of the first edition, 2012).
De la contraportada: This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.
The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.
All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages(CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.
The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results. All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code." Leslie A. Piegl (Review of the first edition, 2012).
"Sobre este título" puede pertenecer a otra edición de este libro.
Detalles bibliográficos
Título: From Curve Fitting to Machine Learning: An ...
Editorial: Springer
Año de publicación: 2016
Encuadernación: Encuadernación de tapa dura
Condición: New
Edición: 2ª Edición
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From Curve Fitting to Machine Learning. An Illustrative Guide to Scientific Data Analysis and Computational Intelligence.
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ISBN 10: 3319325442
ISBN 13: 9783319325446
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ISBN 10: 3319325442
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Serves as introduction to curve fitting, clusteringand machine learning along with topics like mathematical optimization orevolutionary algorithms Outlines all concepts and ideas in a clear cut manner with graphically depictedplausibility argumen. Nº de ref. del artículo: 119612045
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages(CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. ''From curve fitting to machine learning' is . a useful book. . It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results.All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code'. Leslie A. Piegl (Review of the first edition, 2012). Nº de ref. del artículo: 9783319325446
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ISBN 10: 3319325442
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 516 pp. Englisch. Nº de ref. del artículo: 9783319325446
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages(CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. ''From curve fitting to machine learning' is . a useful book. . It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results.All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code'. Leslie A. Piegl (Review of the first edition, 2012). 516 pp. Englisch. Nº de ref. del artículo: 9783319325446
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