Buch, Englisch, Band 9, 231 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 481 g
Theoretical Aspects of Reasoning about Data
Buch, Englisch, Band 9, 231 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 481 g
Reihe: Theory and Decision Library D:
ISBN: 978-0-7923-1472-1
Verlag: Springer
To-date computers are supposed to store and exploit knowledge. At least that is one of the aims of research fields such as Artificial Intelligence and Information Systems. However, the problem is to understand what knowledge means, to find ways of representing knowledge, and to specify automated machineries that can extract useful information from stored knowledge. Knowledge is something people have in their mind, and which they can express through natural language. Knowl edge is acquired not only from books, but also from observations made during experiments; in other words, from data. Changing data into knowledge is not a straightforward task. A set of data is generally disorganized, contains useless details, although it can be incomplete. Knowledge is just the opposite: organized (e.g. laying bare dependencies, or classifications), but expressed by means of a poorer language, i.e. pervaded by imprecision or even vagueness, and assuming a level of granularity. One may say that knowledge is summarized and organized data - at least the kind of knowledge that computers can store.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik Mathematik Mathematik Allgemein Grundlagen der Mathematik
Weitere Infos & Material
I. Theoretical Foundations.- 1. Knowledge.- 2. Imprecise Categories, Approximations and Rough Sets.- 3. Reduction of Knowledge.- 4. Dependencies in Knowledge Base.- 5. Knowledge Representation.- 6. Decision Tables.- 7. Reasoning about Knowledge.- II. Applications.- 8. Decision Making.- 9. Data Analysis.- 10. Dissimilarity Analysis.- 11. Switching Circuits.- 12. Machine Learning.
