| 000 | 04926nam a2201237 i 4500 | ||
|---|---|---|---|
| 001 | 5201503 | ||
| 003 | IEEE | ||
| 005 | 20200421114108.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 101007t20152007njua ob 001 0 eng d | ||
| 020 |
_a9780470140529 _qelectronic |
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| 020 |
_z9780471681823 _qpaper |
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| 020 |
_z0470140526 _qelectronic |
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| 024 | 7 |
_a10.1002/9780470140529 _2doi |
|
| 035 | _a(CaBNVSL)mat05201503 | ||
| 035 | _a(IDAMS)0b0000648104a999 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aTK5102.9 _b.C475 2007eb |
|
| 082 | 0 | 4 |
_a006.3/1 _222 |
| 100 | 1 |
_aCherkassky, Vladimir S. _eauthor. |
|
| 245 | 1 | 0 |
_aLearning from data : _bconcepts, theory, and methods / _cVladimir Cherkassky, Filip Mulier. |
| 250 | _a2nd ed. | ||
| 264 | 1 |
_aHoboken, New Jersey : _bIEEE Press : _cc2007. |
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| 300 |
_a1 PDF (xviii, 538 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 504 | _aIncludes bibliographical references (p. 519-531) and index. | ||
| 505 | 0 | _aProblem statement, classical approaches, and adaptive learning -- Regularization framework -- Statistical learning theory -- Nonlinear optimization strategies -- Methods for data reduction and dimensionality reduction -- Methods for regression -- Classification -- Support vector machines -- Noninductive inference and alternative learning formulations. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aAn interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text. | ||
| 530 | _aAlso available in print. | ||
| 533 |
_aElectronic reproduction. _bPiscataway, N.J. : _cIEEE, _d2010. _nMode of access: World Wide Web. _nSystem requirements: Web browser. _nTitle from title screen (viewed on Oct. 7, 2010). _nAccess may be restricted to users at subscribing institutions. |
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| 538 | _aMode of access: World Wide Web. | ||
| 588 | _aDescription based on PDF viewed 12/19/2015. | ||
| 650 | 0 | _aAdaptive signal processing. | |
| 650 | 0 | _aMachine learning. | |
| 650 | 0 | _aNeural networks (Computer science) | |
| 650 | 0 | _aFuzzy systems. | |
| 655 | 0 | _aElectronic books. | |
| 695 | _aAdaptation model | ||
| 695 | _aAerospace electronics | ||
| 695 | _aAnalytical models | ||
| 695 | _aApproximation algorithms | ||
| 695 | _aApproximation methods | ||
| 695 | _aArtificial intelligence | ||
| 695 | _aArtificial neural networks | ||
| 695 | _aBibliographies | ||
| 695 | _aBiological system modeling | ||
| 695 | _aBiology | ||
| 695 | _aBooks | ||
| 695 | _aBoosting | ||
| 695 | _aClustering algorithms | ||
| 695 | _aClustering methods | ||
| 695 | _aComplexity theory | ||
| 695 | _aConvergence | ||
| 695 | _aData models | ||
| 695 | _aDictionaries | ||
| 695 | _aEigenvalues and eigenfunctions | ||
| 695 | _aEncoding | ||
| 695 | _aEstimation | ||
| 695 | _aFunction approximation | ||
| 695 | _aGenerators | ||
| 695 | _aHafnium | ||
| 695 | _aHumans | ||
| 695 | _aHypercubes | ||
| 695 | _aIndexes | ||
| 695 | _aIterative methods | ||
| 695 | _aKernel | ||
| 695 | _aLearning systems | ||
| 695 | _aLinear approximation | ||
| 695 | _aMachine learning | ||
| 695 | _aMatrix decomposition | ||
| 695 | _aMinimization | ||
| 695 | _aNewton method | ||
| 695 | _aOptimization | ||
| 695 | _aOptimization methods | ||
| 695 | _aParameter estimation | ||
| 695 | _aPattern recognition | ||
| 695 | _aPolynomials | ||
| 695 | _aPredictive models | ||
| 695 | _aPrincipal component analysis | ||
| 695 | _aProbabilistic logic | ||
| 695 | _aProbability | ||
| 695 | _aPrototypes | ||
| 695 | _aRisk management | ||
| 695 | _aSections | ||
| 695 | _aSingular value decomposition | ||
| 695 | _aStatistical learning | ||
| 695 | _aSupport vector machines | ||
| 695 | _aSymmetric matrices | ||
| 695 | _aTaxonomy | ||
| 695 | _aTraining | ||
| 695 | _aTraining data | ||
| 695 | _aUncertainty | ||
| 695 | _aUnsupervised learning | ||
| 695 | _aVector quantization | ||
| 695 | _aVectors | ||
| 695 | _aZinc | ||
| 700 | 1 | _aMulier, Filip. | |
| 710 | 2 |
_aIEEE Xplore (Online service), _edistributor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9780471681823 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5201503 |
| 942 | _cEBK | ||
| 999 |
_c59243 _d59243 |
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