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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Prediction Improvement via Smooth Component Analysis and Neural Network Mixing
verfasst von : Ryszard Szupiluk, Piotr Wojewnik, Tomasz Ząbkowski
Erschienen in: Artificial Neural Networks – ICANN 2006
Verlag: Springer Berlin Heidelberg
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In this paper we derive a novel smooth component analysis algorithm applied for prediction improvement. When many prediction models are tested we can treat their results as multivariate variable with the latent components having constructive or destructive impact on prediction results. The filtration of those destructive components and proper mixing of those constructive should improve final prediction results. The filtration process can be performed by neural networks with initial weights computed from smooth component analysis. The validity and high performance of our concept is presented on the real problem of energy load prediction.