Classification and Modeling with Linguistic Information Granules : Advanced Approaches to Linguistic Data Mining

Hisao Ishibuchi

ISBN 10: 3540207678 / ISBN 13: 9783540207672

Verlag: Springer, Berlin, Springer Berlin Heidelberg, Springer, 2004

Beschreibung

Neuware - Many 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.

Bestandsnummer des Verkäufers 9783540207672

Bibliografische Details

• Titel: Classification and Modeling with Linguistic Information Granules : Advanced Approaches to Linguistic Data Mining
• Verlag: Springer, Berlin, Springer Berlin Heidelberg, Springer
• Erscheinungsjahr: 2004
• Sprache: Englisch
• ISBN-10: 3540207678
• ISBN-13: 9783540207672
• Einband: Buch
• Zustand: Neu
• Abmessungen: 237B x 165 H x 24 T Millimeter Breite: 237 mm, Höhe: 165 mm, Tiefe: 24 mm
• Gewicht: 596 Gramm

Über diesen Titel

Inhaltsangabe

Many 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.