Model selection for Extreme Minimal Learning Machine using sampling
Kärkkäinen, Tommi (2019). Model selection for Extreme Minimal Learning Machine using sampling. In ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN, 391-396. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-18.pdf
© The Author, 2019
A combination of Extreme Learning Machine (ELM) and Minimal Learning Machine (MLM)—to use a distance-based basis from MLM in the ridge regression like learning framework of ELM—was proposed in . In the further experiments with the technique , it was concluded that in multilabel classification one can obtain a good validation error level without overlearning simply by using the whole training data for constructing the basis. Here, we consider possibilities to reduce the complexity of the resulting machine learning model, referred as the Extreme Minimal Leaning Machine (EMLM), by using a bidirectional sampling strategy: To sample both the feature space and the space of observations in order to identify a simpler EMLM without sacrificing its generalization performance.
Parent publication ISBN978-2-87587-065-0
ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Is part of publicationESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Others, AoF; Academy Programme, AoF
Additional information about fundingThe work was supported by the Academy of Finland from the projects 311877 (Demo) and 315550 (HNP-AI).
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