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ORCIDSzilveszter Kovács
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Journal Articles
- 2020
[j5]Mouhammd Al-Kasassbeh, Mohammad Almseidin, Khaled Alrfou
, Szilveszter Kovács:
Detection of IoT-botnet attacks using fuzzy rule interpolation. J. Intell. Fuzzy Syst. 39(1): 421-431 (2020)- [j4]Maen Alzubi, Szilveszter Kovács:
Interpolative fuzzy reasoning method based on the incircle of a generalized triangular fuzzy number. J. Intell. Fuzzy Syst. 39(1): 709-729 (2020) - 2011
- [j3]Domonkos Tikk, Zsolt Csaba Johanyák, Szilveszter Kovács, Kok Wai Wong:
Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines. J. Adv. Comput. Intell. Intell. Informatics 15(3): 254-263 (2011) - [j2]Dávid Vincze, Szilveszter Kovács:
Performance Optimization of the Fuzzy Rule Interpolation Method "FIVE". J. Adv. Comput. Intell. Intell. Informatics 15(3): 313-320 (2011) - 2003
- [j1]Masaharu Sugiyama, Munehiro Goto, Szilveszter Kovács, Tadahiro Matsumoto, Tohru Naoi:
A cropping-robust watermarking method based on singular value decomposition and Haar transformation. Syst. Comput. Jpn. 34(9): 38-47 (2003)
Conference and Workshop Papers
- 2024
- [c33]Tamás Tompa, Szilveszter Kovács:
Integrating Expert Knowledge into Fuzzy Reinforcement Learning. SACI 2024: 333-338 - 2022
- [c32]Dávid Gégény, Szilveszter Kovács:
Fuzzy Interpolation of Fuzzy Rough Sets. FUZZ-IEEE 2022: 1-5 - [c31]Tamas Tompa, Szilveszter Kovács:
Heuristically accelerated FRIQ-learning. SISY 2022: 355-360 - 2021
- [c30]Dávid Vincze, Márta Gácsi, Szilveszter Kovács, Péter Korondi, Ádám Miklósi, Mihoko Niitsuma:
Towards the automatic observation and evaluation of ethologically inspired Human-Robot Interaction. AIM 2021: 586-591 - [c29]Dávid Vincze, Márta Gácsi, Szilveszter Kovács, Mihoko Niitsuma, Péter Korondi, Ádám Miklósi:
Towards the automatic observation and coding of simple behaviours in ethological experiments. SII 2021: 841-842 - [c28]Tamás Tompa, Szilveszter Kovács, Dávid Vincze, Mihoko Niitsuma:
Demonstration of expert knowledge injection in Fuzzy Rule Interpolation based Q-learning. SII 2021: 843-844 - 2017
- [c27]Mohammad Almseidin, Maen Alzubi, Szilveszter Kovács, Mouhammd Alkasassbeh:
Evaluation of machine learning algorithms for intrusion detection system. SISY 2017: 277-282 - 2015
- [c26]Tamas Tompa, Szilveszter Kovács:
Q-leaming vs. FRIQ-leaming in the Maze problem. CogInfoCom 2015: 545-550 - [c25]Gergely I. Molnárka, László T. Kóczy, Szilveszter Kovács:
Evaluating the life quality of the built environment by FRI method. FUZZ-IEEE 2015: 1-7 - [c24]Péter Korondi, Beáta Korcsok, Szilveszter Kovács, Mihoko Niitsuma:
Etho-robotics: What kind of behaviour can we learn from the animals? SyRoCo 2015: 244-255 - 2014
- [c23]Szilveszter Kovács, Ágnes Tóth, László Czap:
Fuzzy model based user adaptive framework for consonant articulation and pronunciation therapy in Hungarian hearing-impaired education. CogInfoCom 2014: 361-366 - [c22]Gergely I. Molnárka, Szilveszter Kovács, László T. Kóczy:
Fuzzy rule interpolation based fuzzy signature structure in building condition evaluation. FUZZ-IEEE 2014: 2214-2221 - 2013
- [c21]Szilveszter Kovács:
Adapting the scale and move FRI for the fuzziness interpolation of the double fuzzy point rule representation. FUZZ-IEEE 2013: 1-7 - 2012
- [c20]Zoltán Krizsán, Szilveszter Kovács:
Fuzzy model based differential IEC for human-system interaction in the VirCA environment. CogInfoCom 2012: 169-173 - [c19]Szilveszter Kovács:
Extending the concept of Fuzzy Rule Interpolation with the interpolation of fuzziness. FUZZ-IEEE 2012: 1-8 - [c18]Dávid Vincze, Szilveszter Kovács, Mihoko Niitsuma, Hideki Hashimoto, Péter Korondi, Márta Gácsi, Ádám Miklósi:
Ethologically inspired human-robot interaction interfaces. HCCE 2012: 51-57 - [c17]Zoltán Krizsán, Szilveszter Kovács:
Fuzzy Rule Interpolation Developer Toolbox Library. SACI 2012: 119-123 - [c16]Takuya Ichikawa, Wataru Beppu, Szilveszter Kovács, Péter Korondi, Hideki Hashimoto, Mihoko Niitsuma:
Ethologically Inspired Human-Robot Communication for Monitoring Support System in Intelligent Space. SyRoCo 2012: 58-63 - 2011
- [c15]Szilveszter Kovács:
Fuzzy rule interpolation in embedded behaviour-based control. FUZZ-IEEE 2011: 436-441 - 2009
- [c14]Zoltán Krizsán, Szilveszter Kovács:
FRI model based pre-evaluation in IEC. SACI 2009: 35-40 - [c13]Dávid Vincze, Szilveszter Kovács:
Fuzzy Rule Interpolation-based Q-learning. SACI 2009: 55-60 - [c12]Szilveszter Kovács, Dávid Vincze, Márta Gácsi, Ádám Miklósi, Péter Korondi:
Interpolation based Fuzzy Automaton for Human-Robot Interaction. SyRoCo 2009: 317-322 - 2007
- [c11]Zsolt Csaba Johanyák, Szilveszter Kovács:
The Effect of Different Fuzzy Partition Parameterization Strategies in Gradient Descent Parameter Identification. SACI 2007: 141-146 - 2006
- [c10]Zsolt Csaba Johanyák, Domonkos Tikk, Szilveszter Kovács, Kok Wai Wong:
Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox. FUZZ-IEEE 2006: 351-357 - 2004
- [c9]Szilveszter Kovács, Péter Baranyi:
Reduction of the dynamic state-space in fuzzy Q-learning. FUZZ-IEEE 2004: 1075-1080 - [c8]Szilveszter Kovács, László T. Kóczy:
Application of interpolation-based fuzzy logic reasoning in behaviour-based control structures. FUZZ-IEEE 2004: 1543-1548 - [c7]Szilveszter Kovács:
Interpolative Fuzzy Reasoning in Behaviour-Based Control. Fuzzy Days 2004: 159-170 - 2003
- [c6]Alex Chong, Tamás D. Gedeon, Szilveszter Kovács, László T. Kóczy:
Sparse fuzzy systems generation and fuzzy rule interpolation: a practical approach. FUZZ-IEEE 2003: 494-499 - 2002
- [c5]Szilveszter Kovács:
Fuzzy reasoning and fuzzy automata in user adaptive emotional and information retrieval systems. SMC (2) 2002: 6 - 2001
- [c4]Szilveszter Kovács:
SVD Reduction in Continuos Environment Reinforcement Learning. Fuzzy Days 2001: 719-738 - 1999
- [c3]Szilveszter Kovács:
Similarity Based System Reconfiguration by Fuzzy Classification and Hierarchical Interpolate Fuzzy Reasoning. Fuzzy Days 1999: 12-19 - 1998
- [c2]Szilveszter Kovács, László T. Kóczy:
Interpolation-based fuzzy logic controller, as a simplified way for constructing the fuzzy rulebase of the path tracking and collision avoidance strategy of an AGV. SMC 1998: 1317-1322 - [c1]Péter Baranyi, Attila Martinovics, Szilveszter Kovács, Domonkos Tikk, Yeung Yam:
A general extension of fuzzy SVD rule base reduction using arbitrary inference algorithm. SMC 1998: 2785-2790
Reference Works
- 2009
- [r1]Szilveszter Kovács:
Fuzzy Rule Interpolation. Encyclopedia of Artificial Intelligence 2009: 728-733
Informal and Other Publications
- 2019
- [i7]Mohammad Almseidin, Szilveszter Kovács:
Intrusion Detection Mechanism Using Fuzzy Rule Interpolation. CoRR abs/1904.08790 (2019) - [i6]Maen Alzubi, Zsolt Csaba Johanyák, Szilveszter Kovács:
Fuzzy Rule Interpolation Methods and Fri Toolbox. CoRR abs/1904.12178 (2019) - [i5]Maen Alzubi, Szilveszter Kovács:
Investigating The Piece-Wise Linearity And Benchmark Related To Koczy-Hirota Fuzzy Linear Interpolation. CoRR abs/1907.01047 (2019) - [i4]Maen Alzubi, Szilveszter Kovács:
Some Considerations and a Benchmark Related to the CNF Property of the Koczy-Hirota Fuzzy Rule Interpolation. CoRR abs/1911.05041 (2019) - [i3]Maen Alzubi, Mohammad Almseidin, Mohd Aaqib Lone, Szilveszter Kovács:
Fuzzy Rule Interpolation Toolbox for the GNU Open-Source OCTAVE. CoRR abs/1912.04999 (2019) - 2018
- [i2]Mohammad Almseidin, Maen Alzubi, Szilveszter Kovács, Mouhammd Alkasassbeh:
Evaluation of Machine Learning Algorithms for Intrusion Detection System. CoRR abs/1801.02330 (2018) - [i1]Mohammad Almseidin, Mouhammd Alkasassbeh, Szilveszter Kovács:
Fuzzy Rule Interpolation and SNMP-MIB for Emerging Network Abnormality. CoRR abs/1811.08954 (2018)
Coauthor Index
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