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008			160413s2016 gw | s |||| 0|eng d
020			_a9783319325453
040			_aES-MaUEC _bspa
050		4	_aQ180.55.S7 _bZ54 2016 EB
082	0	4	_a006.3
100	1		_aZielesny, Achim _0Local _0http://id.loc.gov/authorities/names/no2012054523 _1http://viaf.org/viaf/10531964 _999256
245	1	0	_aFrom Curve Fitting to Machine Learning : _bAn Illustrative Guide to Scientific Data Analysis and Computational Intelligence _cby Achim Zielesny
250			_a2nd ed.
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 498 páginas) _b343 ilustraciones, 200 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_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		7	_9138985 _aInvestigación _2embne
710	2		_aSpringerLink (Online service) _0Local _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-32545-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319325453
907			_a.b12952096 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aEBOOK EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11614663 _z30-06-17
988	0	0	_aEBOOK, EBSPRINGER, GOBI_sep2018
988			_aEbook_one2one
998			_aSI _a_alco _a_vill _b10/2018 _cm _dz _ek _feng _ggw _h0
999			_c85931 _d85931 _x1