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Towards a model of semi-supervised learning for the syntactic pattern recognition-based electrical load prediction system

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Towards a model of semi-supervised learning for the syntactic pattern recognition-based electrical load prediction system

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dc.contributor.author Jurek, Janusz [SAP11015096] pl
dc.contributor.editor Wyrzykowski, Roman pl
dc.contributor.editor Dongarra, Jack pl
dc.contributor.editor Deelman, Ewa pl
dc.contributor.editor Karczewski, Konrad pl
dc.date.accessioned 2018-04-24T09:18:42Z
dc.date.available 2018-04-24T09:18:42Z
dc.date.issued 2018 pl
dc.identifier.isbn 978-3-319-78023-8 pl
dc.identifier.uri https://ruj.uj.edu.pl/xmlui/handle/item/53770
dc.language eng pl
dc.rights Dodaję tylko opis bibliograficzny *
dc.rights.uri *
dc.title Towards a model of semi-supervised learning for the syntactic pattern recognition-based electrical load prediction system pl
dc.type BookSection pl
dc.pubinfo Cham : Springer pl
dc.description.physical 533-543 pl
dc.abstract.en The paper is devoted to one of the key open problems of development of SPRELP system (the Syntactic Pattern Recognitionbased Electrical Load Prediction System). The main module of SPRELP System is based on a GDPLL(k) grammar that is built according to the unsupervised learning paradigm. The GDPLL(k) grammar is generated by a grammatical inference algorithm. The algorithm doesn't take into account an additional knowledge (the knowledge is partial and corresponds only to some examples) provided by a human expert. The accuracy of the forecast could be better if we took advantage of this knowledge.The problem of how to construct the model of a semi-supervised learning for SPRLP system that includes the additional expert knowledge is discussed in the paper. We also present several possible solutions. pl
dc.subject.en syntactic pattern recognition pl
dc.subject.en grammatical inference pl
dc.subject.en semi-supervised learning pl
dc.subject.en electrical load forecast pl
dc.description.series Theoretical Computer Science and General Issues; vol. 10777 pl
dc.description.volume 1 pl
dc.description.publication 0,7 pl
dc.description.conftype international pl
dc.identifier.doi 10.1007/978-3-319-78024-5_46 pl
dc.identifier.eisbn 978-3-319-78024-5 pl
dc.title.container Parallel processing and applied mathematics : 12th International Conference, PPAM 2017, Lublin, Poland, September 10-13, 2017, revised selected papers pl
dc.language.container eng pl
dc.affiliation Wydział Zarządzania i Komunikacji Społecznej : Katedra Systemów Informatycznych pl
dc.subtype ConferenceProceedings pl
dc.conference 12th International Conference PPAM 2017; 2017-09-10; 2017-09-13; Lublin; Polska; indeksowana w Web of Science; ; ; ; PPAM; pl
dc.rights.original bez licencji pl
dc.sourceinfo liczba autorów 170; liczba stron 685; liczba arkuszy wydawniczych 45,6; pl
dc.publisher.ministerial Springer pl


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