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Rudiments of rough sets
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“Some systems integrate basic learning capabilities [72] through feed-back loops [73,74], heuristics [75], or updated context buffers to refine behavior over time, particularly in settings like personalized recommendations or conversation flow management [76– 78].”
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Metadata
- Authors
- Pawlak, Z., & Skowron, A.
- Affiliations
- University of Warsaw (PL)
- Corresponding author
- Skowron, Andrzej
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 177
- Issue
- 1
- Pages
- 3-27
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2143451122
- Cited by
- 2,070
- References
- 352
- Citations by year
- 2026: 28 · 2025: 49 · 2024: 61 · 2023: 65 · 2022: 67 · 2021: 89 · 2020: 92 · 2019: 102 · 2018: 108 · 2017: 85 · 2016: 120 · 2015: 125 · 2014: 139 · 2013: 129 · 2012: 182
- FWCI
- 111.73
- Type
- journal article
- Language
- en
- Open access
- green
- bcpw.bg.pw.edu.pl
- Locations
- Information Sciences (publishedVersion)
- Our Digital Library (Warsaw University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Rough Sets and Fuzzy Logic
- Social Sciences › Decision Sciences › Management Science and Operations Research › Fuzzy and Soft Set Theory
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Algebra and Logic
- Keywords
- rough set theory, artificial intelligence, knowledge discovery, decision analysis, expert systems, pattern recognition, inductive reasoning, mereology
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