|
|
|
|
| LEADER |
04280nmm a2200301 u 4500 |
| 001 |
EB000617655 |
| 003 |
EBX01000000000000000470737 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
140122 ||| eng |
| 020 |
|
|
|a 9781447132387
|
| 100 |
1 |
|
|a Ziarko, Wojciech P.
|e [editor]
|
| 245 |
0 |
0 |
|a Rough Sets, Fuzzy Sets and Knowledge Discovery
|h Elektronische Ressource
|b Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD’93), Banff, Alberta, Canada, 12–15 October 1993
|c edited by Wojciech P. Ziarko
|
| 250 |
|
|
|a 1st ed. 1994
|
| 260 |
|
|
|a London
|b Springer London
|c 1994, 1994
|
| 300 |
|
|
|a X, 476 p. 18 illus
|b online resource
|
| 505 |
0 |
|
|a A Special Case of Interval Structures -- A Pure Logic-Algebraic Analysis of Rough Top and Rough Bottom Equalities -- A Novel Approach to the Minimal Cover Problem -- Algebraic Structures of Rough Sets -- Rough Concept Analysis -- Rough Approximate Operators: Axiomatic Rough Set Theory -- Finding Reducts in Composed Information Systems -- PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory -- Comparison of Machine Learning and Knowledge Acquisition Methods of Rule Induction Based on Rough Sets -- AQ, Rough Sets, and Matroid Theory -- Rough Classifiers --
|
| 505 |
0 |
|
|a A Rough Sets Approach --
|
| 505 |
0 |
|
|a Recent Progress and Challenges -- Rough Sets and Knowledge Discovery: An Overview -- Search for Concepts and Dependencies in Databases -- Rough Sets and Concept Lattices -- Human-Computer Interfaces: DBLEARN and SystemX -- A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN -- Knowledge Recognition, Rough Sets, and Formal Concept Lattices -- Quantifying Uncertainty of Knowledge Discovered from Databases -- Temporal Rules Discovery Using Datalogic/R+ with Stock Market Data -- A System Architecture for Database Mining Applications -- An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases -- A Rough Set Model for Relational Databases -- Data Filtration: A Rough Set Approach -- Automated Discovery of Empirical Laws in a Science Laboratory -- Hard and Soft Sets -- Rough Set Analysis of Multi-Attribute Decision Problems -- Rough Set Semantics for Non-Classical Logics --
|
| 505 |
0 |
|
|a An Expert System for Environmental Protection -- Author Index
|
| 653 |
|
|
|a Artificial Intelligence
|
| 653 |
|
|
|a Artificial intelligence
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b SBA
|a Springer Book Archives -2004
|
| 490 |
0 |
|
|a Workshops in Computing
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4471-3238-7?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 006.3
|
| 520 |
|
|
|a The objective of this book is two-fold. Firstly, it is aimed at bringing to gether key research articles concerned with methodologies for knowledge discovery in databases and their applications. Secondly, it also contains articles discussing fundamentals of rough sets and their relationship to fuzzy sets, machine learning, management of uncertainty and systems of logic for formal reasoning about knowledge. Applications of rough sets in different areas such as medicine, logic design, image processing and expert systems are also represented. The articles included in the book are based on selected papers presented at the International Workshop on Rough Sets and Knowledge Discovery held in Banff, Canada in 1993. The primary methodological approach emphasized in the book is the mathematical theory of rough sets, a relatively new branch of mathematics concerned with the modeling and analysis of classification problems with imprecise, uncertain, or incomplete information. The methods of the theory of rough sets have applications in many sub-areas of artificial intelligence including knowledge discovery, machine learning, formal reasoning in the presence of uncertainty, knowledge acquisition, and others. This spectrum of applications is reflected in this book where articles, although centered around knowledge discovery problems, touch a number of related issues. The book is intended to provide an important reference material for students, researchers, and developers working in the areas of knowledge discovery, machine learning, reasoning with uncertainty, adaptive expert systems, and pattern classification
|