000 03817nam a22004695i 4500
001 978-3-540-75390-2
003 DE-He213
005 20170628034803.0
007 cr nn 008mamaa
008 100301s2008 gw | s |||| 0|eng d
020 _a9783540753902
_9978-3-540-75390-2
024 7 _a10.1007/978-3-540-75390-2
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aDiederich, Joachim.
_eeditor.
245 1 0 _aRule Extraction from Support Vector Machines
_h[electronic resource] /
_cedited by Joachim Diederich.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _aXII, 262 p. 55 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v80
505 0 _aRule Extraction from Support Vector Machines: An Introduction -- Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring -- Algorithms and Techniques -- Rule Extraction for Transfer Learning -- Rule Extraction from Linear Support Vector Machines via Mathematical Programming -- Rule Extraction Based on Support and Prototype Vectors -- SVMT-Rule: Association Rule Mining Over SVM Classification Trees -- Prototype Rules from SVM -- Applications -- Prediction of First-Day Returns of Initial Public Offering in the US Stock Market Using Rule Extraction from Support Vector Machines -- Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction -- Rule Extraction from SVM for Protein Structure Prediction.
520 _aSupport vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540753896
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v80
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-75390-2
912 _aZDB-2-ENG
999 _c21513
_d21513