Towards a Model of Semi-supervised Learning for the Syntactic Pattern Recognition-Based Electrical Load Prediction System
| Autor: | Janusz Jurek |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
semi-supervised learning
electrical load forecast Electrical load Grammar Computer science business.industry media_common.quotation_subject syntactic pattern recognition 0102 computer and information sciences 02 engineering and technology Construct (python library) Semi-supervised learning Syntactic pattern recognition 01 natural sciences Grammar induction grammatical inference 010201 computation theory & mathematics 0202 electrical engineering electronic engineering information engineering Key (cryptography) Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence business media_common |
| Zdroj: | Parallel Processing and Applied Mathematics ISBN: 9783319780238 PPAM (1) |
| DOI: | 10.1007/978-3-319-78024-5_46 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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