000			03594nam a22005175i 4500
001			978-3-540-26875-8
003			DE-He213
005			20161121230527.0
007			cr nn 008mamaa
008			100301s2005 gw | s |||| 0|eng d
020			_a9783540268758 _9978-3-540-26875-8
024	7		_a10.1007/b138232 _2doi
050		4	_aP98-98.5
072		7	_aCFX _2bicssc
072		7	_aLAN009000 _2bisacsh
072		7	_aCOM018000 _2bisacsh
082	0	4	_a410.285 _223
100	1		_aIshibuchi, Hisao. _eauthor.
245	1	0	_aClassification and Modeling with Linguistic Information Granules _h[electronic resource] : _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	_aLinguistics.
650		0	_aComputers.
650		0	_aArtificial intelligence.
650		0	_aComputational linguistics.
650	1	4	_aLinguistics.
650	2	4	_aComputational Linguistics.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aModels and Principles.
700	1		_aNakashima, Tomoharu. _eauthor.
700	1		_aNii, Manabu. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783540207672
830		0	_aAdvanced Information Processing
856	4	0	_uhttp://dx.doi.org/10.1007/b138232
912			_aZDB-2-SCS
950			_aComputer Science (Springer-11645)
999			_c500087 _d500087