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Classification and Modeling with Linguistic Information Granules Advanced Approaches to Linguistic Data Mining /
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, i...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2005.
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| Edición: | 1st ed. 2005. |
| Colección: | Advanced Information Processing
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
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| 001 | 978-3-540-26875-8 | ||
| 003 | DE-He213 | ||
| 005 | 20220118094350.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2005 gw | s |||| 0|eng d | ||
| 020 | |a 9783540268758 |9 978-3-540-26875-8 | ||
| 024 | 7 | |a 10.1007/b138232 |2 doi | |
| 050 | 4 | |a P98-98.5 | |
| 072 | 7 | |a CFX |2 bicssc | |
| 072 | 7 | |a LAN009000 |2 bisacsh | |
| 072 | 7 | |a CFX |2 thema | |
| 082 | 0 | 4 | |a 410.285 |2 23 |
| 100 | 1 | |a Ishibuchi, Hisao. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Classification and Modeling with Linguistic Information Granules |h [electronic resource] : |b Advanced Approaches to Linguistic Data Mining / |c by Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii. |
| 250 | |a 1st ed. 2005. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2005. | |
| 300 | |a XII, 308 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Advanced Information Processing | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Computational linguistics. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer science. | |
| 650 | 1 | 4 | |a Computational Linguistics. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Models of Computation. |
| 700 | 1 | |a Nakashima, Tomoharu. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Nii, Manabu. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783540800644 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642058608 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540207672 |
| 830 | 0 | |a Advanced Information Processing | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/b138232 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||