000			03057nam a22004455i 4500
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005			20231104114440.0
007			cr nn 008mamaa
008			151022s2015 gw | s |||| 0|eng d
020			_a9783319219424 _z978-3-319-21942-4
024	7		_a10.1007/978-3-319-21942-4 _2doi
050		4	_aQ334-342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema006.3 _223
100	1		_aKenesei, Tamás. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aInterpretability of Computational Intelligence-Based Regression Models / _cby Tamás Kenesei, János Abonyi.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science, _x2191-5768
505	0		_aIntroduction -- Interpretability of Hinging Hyperplanes -- Interpretability of Neural Networks -- Interpretability of Support Vector Machines -- Summary.
520			_aThe key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression. The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.
650		0	_aArtificial intelligence.
650		0	_aEngineering.
650		0	_aData mining.
650	1	4	_aArtificial Intelligence. _0http://scigraph.springernature.com/things/product-market-codes/I21000
650	2	4	_aComputational Intelligence. _0http://scigraph.springernature.com/things/product-market-codes/T11014
650	2	4	_aData Mining and Knowledge Discovery. _0http://scigraph.springernature.com/things/product-market-codes/I18030
700	1		_aAbonyi, János. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-319-21942-4 _3Springer eBooks _zOnline access link to the resource
912			_aZDB-2-SCS
999			_c200434587 _d52799
942			_2lcc _cEBK
041			_aeng