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Towards a model of semi-supervised learning for the syntactic pattern recognition-based electrical load prediction system
Author
Jurek Janusz
Editor
Wyrzykowski Roman
Dongarra Jack
Deelman Ewa
Karczewski Konrad
Book title / Journal title
Parallel processing and applied mathematics : 12th International Conference, PPAM 2017, Lublin, Poland, September 10-13, 2017, revised selected papers
publisher
Cham : Springer
Volume
1
Pages
533-543
ISBN
978-3-319-78023-8
eISBN
978-3-319-78024-5
Series
Theoretical Computer Science and General Issues
Number of serie
vol. 10777
Keywords in English
syntactic pattern recognition
grammatical inference
semi-supervised learning
electrical load forecast
Language
English
Book language / Journal language
English
Abstract in English
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.
Conference
International Conference on Parallel Processing and Applied Mathematics
Conference start date
2017-09-10
End date conference
2017-09-13
Conference city
Lublin
Conference country
Polska
Conference type
international
Affiliation
Wydział Zarządzania i Komunikacji Społecznej : Katedra Systemów Informatycznych
| 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.affiliation | Wydział Zarządzania i Komunikacji Społecznej : Katedra Systemów Informatycznych | pl |
| dc.conference | International Conference on Parallel Processing and Applied Mathematics | pl |
| dc.conference.city | Lublin | |
| dc.conference.country | Polska | |
| dc.conference.datefinish | 2017-09-13 | |
| dc.conference.datestart | 2017-09-10 | |
| dc.conference.indexwos | true | |
| dc.contributor.author | Jurek, Janusz - 128557 | 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.description.conftype | international | pl |
| dc.description.physical | 533-543 | pl |
| dc.description.publication | 0,7 | pl |
| dc.description.series | Theoretical Computer Science and General Issues | |
| dc.description.seriesnumber | vol. 10777 | |
| dc.description.volume | 1 | pl |
| dc.identifier.doi | 10.1007/978-3-319-78024-5_46 | pl |
| dc.identifier.eisbn | 978-3-319-78024-5 | 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.language.container | eng | pl |
| dc.pubinfo | Cham : Springer | pl |
| dc.publisher.ministerial | Springer | pl |
| dc.rights | Dodaję tylko opis bibliograficzny | * |
| dc.rights.licence | bez licencji | |
| dc.rights.uri | * | |
| dc.sourceinfo | liczba autorów 170; liczba stron 685; liczba arkuszy wydawniczych 45,6; | 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.subtype | ConferenceProceedings | pl |
| dc.title | Towards a model of semi-supervised learning for the syntactic pattern recognition-based electrical load prediction system | 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.type | BookSection | pl |
| dspace.entity.type | Publication |
dc.abstract.enpl
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. dc.affiliationpl
Wydział Zarządzania i Komunikacji Społecznej : Katedra Systemów Informatycznych dc.conferencepl
International Conference on Parallel Processing and Applied Mathematics dc.conference.city
Lublin dc.conference.country
Polska dc.conference.datefinish
2017-09-13 dc.conference.datestart
2017-09-10 dc.conference.indexwos
true dc.contributor.authorpl
Jurek, Janusz - 128557 dc.contributor.editorpl
Wyrzykowski, Roman dc.contributor.editorpl
Dongarra, Jack dc.contributor.editorpl
Deelman, Ewa dc.contributor.editorpl
Karczewski, Konrad dc.date.accessioned
2018-04-24T09:18:42Z dc.date.available
2018-04-24T09:18:42Z dc.date.issuedpl
2018 dc.description.conftypepl
international dc.description.physicalpl
533-543 dc.description.publicationpl
0,7 dc.description.series
Theoretical Computer Science and General Issues dc.description.seriesnumber
vol. 10777 dc.description.volumepl
1 dc.identifier.doipl
10.1007/978-3-319-78024-5_46 dc.identifier.eisbnpl
978-3-319-78024-5 dc.identifier.isbnpl
978-3-319-78023-8 dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/53770 dc.languagepl
eng dc.language.containerpl
eng dc.pubinfopl
Cham : Springer dc.publisher.ministerialpl
Springer dc.rights*
Dodaję tylko opis bibliograficzny dc.rights.licence
bez licencji dc.rights.uri*
dc.sourceinfopl
liczba autorów 170; liczba stron 685; liczba arkuszy wydawniczych 45,6; dc.subject.enpl
syntactic pattern recognition dc.subject.enpl
grammatical inference dc.subject.enpl
semi-supervised learning dc.subject.enpl
electrical load forecast dc.subtypepl
ConferenceProceedings dc.titlepl
Towards a model of semi-supervised learning for the syntactic pattern recognition-based electrical load prediction system dc.title.containerpl
Parallel processing and applied mathematics : 12th International Conference, PPAM 2017, Lublin, Poland, September 10-13, 2017, revised selected papers dc.typepl
BookSection dspace.entity.type
Publication Affiliations
Wydział Zarządzania i Komunikacji Społecznej
Jurek, Janusz
No affiliation
Wyrzykowski, Roman
Dongarra, Jack
Deelman, Ewa
Karczewski, Konrad