From Curve Fitting to Machine Learning (Record no. 59148)
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| 000 -LEADER | |
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| fixed length control field | 04099nam a22005895i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-32545-3 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421112556.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 160413s2016 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319325453 |
| -- | 978-3-319-32545-3 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Zielesny, Achim. |
| 245 10 - TITLE STATEMENT | |
| Title | From Curve Fitting to Machine Learning |
| Sub Title | An Illustrative Guide to Scientific Data Analysis and Computational Intelligence / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. 2016. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XV, 498 p. 343 illus., 200 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Intelligent Systems Reference Library, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Curve Fitting -- Clustering -- Machine Learning -- Discussion -- CIP -Computational Intelligence Packages. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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). |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-32545-3 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2016. |
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| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical optimization. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Applied mathematics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering mathematics. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Appl.Mathematics/Computational Methods of Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big Data/Analytics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Optimization. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1868-4394 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
No items available.