Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Ivan Bruha"'
Publikováno v:
Intelligent Data Analysis. 2000, Vol. 4 Issue 5, p445. 16p.
Autor:
Ivan Bruha
Publikováno v:
Journal of Intelligent Information Systems. 22:71-87
Efficient robust data mining algorithms should comprise some routines for processing unknown (missing) attribute values when acquiring knowledge from real-world databases because these data usually contain a certain percentage of missing values. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3118131928546010faaea0726e5c852
https://doi.org/10.1023/a:1025880714026
https://doi.org/10.1023/a:1025880714026
Autor:
Josef Tkadlec, Ivan Bruha
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 17:581-600
This paper deals with the multiple-rule problem which arises when several decision rules (of different classes) match ("fire" for) an input to-be-classified (unseen) object. The paper focuses on formal aspects and theoretical methodology for the abov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b493992c3cfb05d3b57b9350fce6d32
https://doi.org/10.1142/s021800140300254x
https://doi.org/10.1142/s021800140300254x
Autor:
Ivan Bruha, Josef Tkadlec
Publikováno v:
Intelligent Data Analysis. 7:99-124
A rule-inducing learning algorithm may yield either an ordered or unordered set of decision rules. The latter seems to be more understandable by humans and directly applicable in most expert systems or decision-supporting ones. However, classificatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24619396ad31ede1d694ac2e22062841
https://doi.org/10.3233/ida-2003-7203
https://doi.org/10.3233/ida-2003-7203
Autor:
Ivan Bruha, A. Famili
Publikováno v:
ACM SIGKDD Explorations Newsletter. 2:110-114
This article surveys the contents of the workshop Post-Processing in Machine Learning and Data Mining: Interpretation, Visualization, Integration, and Related Topics within KDD-2000: The Sixth ACM SIGKDD International Conference on Knowledge Discover
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97bc090b18ebb5a3c7f6331a7ff3263b
https://doi.org/10.1145/380995.381059
https://doi.org/10.1145/380995.381059
Autor:
Ivan Bruha, Petr Berka
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 12:1017-1032
The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an importan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d073d4fc3d569c83a944cbfe955a0b0
https://doi.org/10.1142/s0218001498000567
https://doi.org/10.1142/s0218001498000567
Autor:
Ivan Bruha, Frantisek Franek
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 10:939-955
Simple inductive learning algorithms assume that all attribute values are available. The well-known Quinlan's paper1 discusses quite a few routines for the processing of unknown attribute values in the TDIDT family and analyzes seven of them. This pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02abf72b11f55763e62eadaea55266d6
https://doi.org/10.1142/s0218001496000530
https://doi.org/10.1142/s0218001496000530
Autor:
Ivan Bruha, Sylva Kocková
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 10:239-250
The empirical inductive algorithms that utilize the covering paradigm (such as the AQ x and CN x families of inductive systems) comprise various heuristics and statistical tools so that the core of the covering paradigm remains often quite hidden. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9ba6dedb0413558d87923c2dbd47fff3
https://doi.org/10.1142/s0218001496000189
https://doi.org/10.1142/s0218001496000189