Rough Sets
Theoretical Aspects of Reasoning about Data
Seiten
2012
|
Softcover reprint of the original 1st ed. 1991
Springer (Verlag)
978-94-010-5564-2 (ISBN)
Springer (Verlag)
978-94-010-5564-2 (ISBN)
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. One may say that knowledge is summarized and organized data - at least the kind of knowledge that computers can store.
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.
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.
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.
| Reihe/Serie | Theory and Decision Library D ; 9 |
|---|---|
| Zusatzinfo | XVI, 231 p. |
| Verlagsort | Dordrecht |
| Sprache | englisch |
| Maße | 160 x 240 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Allgemeines / Lexika | |
| Mathematik / Informatik ► Mathematik ► Logik / Mengenlehre | |
| Wirtschaft ► Betriebswirtschaft / Management | |
| ISBN-10 | 94-010-5564-5 / 9401055645 |
| ISBN-13 | 978-94-010-5564-2 / 9789401055642 |
| Zustand | Neuware |
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