Wszystkie pola: bayes - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Data analysis and flow graphs
Autorzy:: Pawlak, Z.
Powiązania:: https://bibliotekanauki.pl/articles/308986.pdf
Data publikacji:: 2004
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: data mining
data independence
flow graph
Bayes' rule
Opis:: In this paper we present a new approach to data analysis based on flow distribution study in a flow network. Branches of the flow graph are interpreted as decision rules, whereas the flow graph is supposed to describe a decision algorithm. We propose to model decision processes as flow graphs and analyze decisions in terms of flow spreading in the graph.
Źródło:: Journal of Telecommunications and Information Technology; 2004, 3; 18-22
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Possibilistic approach to Bayes decisions
Autorzy:: Hryniewicz, O.
Powiązania:: https://bibliotekanauki.pl/articles/307688.pdf
Data publikacji:: 2003
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: decyzja optymalna
optimal decisions
imprecise information
fuzzy risks
possibility indices
Opis:: The decision problems are considered when the prior probabilistic information about the state of nature and decision maker's utility function are imprecisely defined. In such a case the risks (or the expected utility) of considered decisions are also imprecisely defined. We propose two-step procedure for finding the optimal decision. First, we order possible decisions using the lambda-average ranking method by Campos and Gonzalez [1]. Then we use possibilistic possibility of dominance and necessity of strict dominance indices proposed by Dubois and Prade [3] for the comparison of consequences of the most promising solutions.
Źródło:: Journal of Telecommunications and Information Technology; 2003, 3; 3-8
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Comparative Study of Supervised Learning Methods for Malware Analysis
Autorzy:: Kruczkowski, M.
Niewiadomska-Szynkiewicz, E.
Powiązania:: https://bibliotekanauki.pl/articles/309481.pdf
Data publikacji:: 2014
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: data classification
k-Nearest Neighbors
malware analysis
Naive Bayes
support vector machine (SVM)
Opis:: Malware is a software designed to disrupt or even damage computer system or do other unwanted actions. Nowadays, malware is a common threat of the World Wide Web. Anti-malware protection and intrusion detection can be significantly supported by a comprehensive and extensive analysis of data on the Web. The aim of such analysis is a classification of the collected data into two sets, i.e., normal and malicious data. In this paper the authors investigate the use of three supervised learning methods for data mining to support the malware detection. The results of applications of Support Vector Machine, Naive Bayes and k-Nearest Neighbors techniques to classification of the data taken from devices located in many units, organizations and monitoring systems serviced by CERT Poland are described. The performance of all methods is compared and discussed. The results of performed experiments show that the supervised learning algorithms method can be successfully used to computer data analysis, and can support computer emergency response teams in threats detection.
Źródło:: Journal of Telecommunications and Information Technology; 2014, 4; 24-33
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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