000 03071nam a22005295i 4500
001 978-3-319-21942-4
003 DE-He213
005 20200421112552.0
007 cr nn 008mamaa
008 151022s2015 gw | s |||| 0|eng d
020 _a9783319219424
_9978-3-319-21942-4
024 7 _a10.1007/978-3-319-21942-4
_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 _aKenesei, Tam�as.
_eauthor.
245 1 0 _aInterpretability of Computational Intelligence-Based Regression Models
_h[electronic resource] /
_cby Tam�as Kenesei, J�anos Abonyi.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aX, 82 p. 34 illus., 14 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 _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 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aAbonyi, J�anos.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319219417
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-21942-4
912 _aZDB-2-SCS
942 _cEBK
999 _c58899
_d58899