Classification and Modeling with Linguistic Information Granules

Advanced Approaches to Linguistic Data Mining
1. Auflage. Softcover version of original hardcover Auflage 2005
ISBN: 978-3-642-05860-8
Verlag: Springer

Buch, Englisch, 308 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 487 g

Reihe: Advanced Information Processing

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.

Zielgruppe

Research

Autoren/Hrsg.

Ishibuchi, Hisao

Nii, Manabu

Nakashima, Tomoharu

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Linguistic 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.