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Wyszukujesz frazę "Kazienko, P." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Mining indirect association rules for web recommendation
Autorzy:
Kazienko, P.
Powiązania:
https://bibliotekanauki.pl/articles/907860.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
reguły asocjacji
system zalecany
badanie sieci
association rules
indirect association rules
recommender system
web mining
web usage mining
Opis:
Classical association rules, here called 'direct', reflect relationships existing between items that relatively often co-occur in common transactions. In the web domain, items correspond to pages and transactions to user sessions. The main idea of the new approach presented is to discover indirect associations existing between pages that rarely occur together but there are other, 'third' pages, called transitive, with which they appear relatively frequently. Two types of indirect associations rules are described in the paper: partial indirect associations and complete ones. The former respect single transitive pages, while the latter cover all existing transitive pages. The presented IDARM* Algorithm extracts complete indirect association rules with their important measure-confidence-using pre-calculated direct rules. Both direct and indirect rules are joined into one set of complex association rules, which may be used for the recommendation of web pages. Performed experiments revealed the usefulness of indirect rules for the extension of a typical recommendation list. They also deliver new knowledge not available to direct ones. The relation between ranking lists created on the basis of direct association rules as well as hyperlinks existing on web pages is also examined.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2009, 19, 1; 165-186
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-label classification using error correcting output codes
Autorzy:
Kajdanowicz, T.
Kazienko, P.
Powiązania:
https://bibliotekanauki.pl/articles/331286.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maszyna ucząca się
uczenie nadzorowane
metoda agregacji
struktura ramowa
machine learning
supervised learning
multilabel classification
error correcting output codes
ECOC
ensemble methods
binary relevance
framework
Opis:
A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. The experimental results revealed that (i) the Bode-Chaudhuri-Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 829-840
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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