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Wyszukujesz frazę "Piegat, A" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
On practical problems with the explanation of the difference between possibility and probability
Autorzy:
Piegat, A.
Powiązania:
https://bibliotekanauki.pl/articles/970096.pdf
Data publikacji:
2005
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
system rozmyty
arytmetyka rozmyta
możliwość
prawdopodobieństwo
fuzzy systems
fuzzy arithmetic
possibility
probability
Opis:
In his famous paper "Fuzzy Sets as a Basis for a Theory of Possibility" (Zadeh, 1978) Professor Lofti Zadeh introduced the notion of possibility distribution [pi]x and tlie concept of possibility measure. He denned in the paper the possibility distribution function to be numerically equal to the membership function ([pi]x = [my]F). In this paper Professor Zadeh draws the special attention of the reader to the fact that: "... there is a fundamental difference between probability and possibility". To explain this difference he had given a special example illustrating the difference, which then was cited by many authors of books on Fuzzy Set Theory and gained great importance for understanding the notion of possibility. In the paper the author presents his doubts as to this important example, explains why it is incorrect and gives a correct version of the example based on the notion of possibility distribution of Dubois and Prade.
Źródło:
Control and Cybernetics; 2005, 34, 2; 505-524
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Specialized, MSE-optimal m-estimators of the rule probability especially suitable for machine learning
Autorzy:
Piegat, A.
Landowski, M.
Powiązania:
https://bibliotekanauki.pl/articles/205508.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
machine learning
rule probability
probability estimation
m-estimators
decision trees
rough set theory
Opis:
The paper presents an improved sample based rule- probability estimation that is an important indicator of the rule quality and credibility in systems of machine learning. It concerns rules obtained, e.g., with the use of decision trees and rough set theory. Particular rules are frequently supported only by a small or very small number of data pieces. The rule probability is mostly investigated with the use of global estimators such as the frequency-, the Laplace-, or the m-estimator constructed for the full probability interval [0,1]. The paper shows that precision of the rule probability estimation can be considerably increased by the use of m-estimators which are specialized for the interval [phmin, phmax] given by the problem expert. The paper also presents a new interpretation of the m-estimator parameters that can be optimized in the estimators.
Źródło:
Control and Cybernetics; 2014, 43, 1; 133-160
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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