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001 978-3-319-32545-3
003 DE-He213
005 20200421112556.0
007 cr nn 008mamaa
008 160413s2016 gw | s |||| 0|eng d
020 _a9783319325453
_9978-3-319-32545-3
024 7 _a10.1007/978-3-319-32545-3
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aZielesny, Achim.
_eauthor.
245 1 0 _aFrom Curve Fitting to Machine Learning
_h[electronic resource] :
_bAn Illustrative Guide to Scientific Data Analysis and Computational Intelligence /
_cby Achim Zielesny.
250 _a2nd ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 498 p. 343 illus., 200 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v109
505 0 _aIntroduction -- Curve Fitting -- Clustering -- Machine Learning -- Discussion -- CIP -Computational Intelligence Packages.
520 _aThis 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).
650 0 _aComputer science.
650 0 _aBig data.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aMathematical optimization.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aBig Data/Analytics.
650 2 4 _aOptimization.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319325446
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v109
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-32545-3
912 _aZDB-2-ENG
942 _cEBK
999 _c59148
_d59148