MARC Record from harvard_bibliographic_metadata

LEADER: 03355nam a22004695a 4500
001 014277726-9
005 20150113020620.0
008 100301s2005 gw | s ||0| 0|eng d
020 $a9783540268758
020 $a9783540268758
020 $a9783540207672
024 7 $a10.1007/b138232$2doi
035 $a(Springer)9783540268758
040 $aSpringer
050 4 $aQ334-342
050 4 $aTJ210.2-211.495
072 7 $aUYQ$2bicssc
072 7 $aTJFM1$2bicssc
072 7 $aCOM004000$2bisacsh
082 04 $a006.3$223
100 1 $aIshibuchi, Hisao.$eauthor.
245 10 $aClassification and Modeling with Linguistic Information Granules :$bAdvanced Approaches to Linguistic Data Mining /$cby Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii.
264 1 $aBerlin, Heidelberg :$bSpringer Berlin Heidelberg,$c2005.
300 $aXII, 308 p.$bonline resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file$bPDF$2rda
490 1 $aAdvanced Information Processing
505 0 $aLinguistic Information Granules -- Pattern Classification with Linguistic Rules -- Learning of Linguistic Rules -- Input Selection and Rule Selection -- Genetics-Based Machine Learning -- Multi-Objective Design of Linguistic Models -- Comparison of Linguistic Discretization with Interval Discretization -- Modeling with Linguistic Rules -- Design of Compact Linguistic Models -- Linguistic Rules with Consequent Real Numbers -- Handling of Linguistic Rules in Neural Networks -- Learning of Neural Networks from Linguistic Rules -- Linguistic Rule Extraction from Neural Networks -- Modeling of Fuzzy Input—Output Relations.
520 $aMany approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod­ els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe­ matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com­ puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter­ net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model­ ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
650 0 $aComputer science.
650 0 $aArtificial intelligence.
650 14 $aComputer Science.
650 24 $aArtificial Intelligence (incl. Robotics).
650 24 $aModels and Principles.
700 1 $aNakashima, Tomoharu.$eauthor.
700 1 $aNii, Manabu.$eauthor.
776 08 $iPrinted edition:$z9783540207672
830 0 $aAdvanced Information Processing
988 $a20150113
906 $0VEN