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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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Parallel Processing and Applied Mathematics (PPAM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10777))

Abstract

The paper is devoted to one of the key open problems of development of SPRELP system (the Syntactic Pattern Recognition-based 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.

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Authors and Affiliations

  1. Information Technology Systems Department, Jagiellonian University, ul. prof. St. Łojasiewicza 4, Cracow, 30-348, Poland

    Janusz Jurek

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  1. Janusz Jurek
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Correspondence to Janusz Jurek .

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Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

  3. University of Southern California, Marina Del Rey, California, USA

    Ewa Deelman

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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Jurek, J. (2018). Towards a Model of Semi-supervised Learning for the Syntactic Pattern Recognition-Based Electrical Load Prediction System. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10777. Springer, Cham. https://doi.org/10.1007/978-3-319-78024-5_46

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  • DOI: https://doi.org/10.1007/978-3-319-78024-5_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78023-8

  • Online ISBN: 978-3-319-78024-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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