Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Gygax, Gregory | - |
| dc.contributor.author | Füchslin, Rudolf Marcel | - |
| dc.contributor.author | Ott, Thomas | - |
| dc.date.accessioned | 2020-11-19T10:42:59Z | - |
| dc.date.available | 2020-11-19T10:42:59Z | - |
| dc.date.issued | 2020-11 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/20862 | - |
| dc.description.abstract | We present the idea of a self-organized division of labor in networks of forecasting models. We find that the principles of self-organizing maps provide a good starting point for building resilient machine learning systems based on our idea. The potential of the idea, benefits and challenges are discussed by means of two toy-like problems. | de_CH |
| dc.language.iso | en | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Self-organization | de_CH |
| dc.subject | Resilient machine learning | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | Self-organized division of labor in networks of forecasting models for time series with regime switches | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | Life Sciences und Facility Management | de_CH |
| zhaw.organisationalunit | Institut für Computational Life Sciences (ICLS) | de_CH |
| zhaw.conference.details | 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), Online Conference, 16-19 November 2020 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 281 | de_CH |
| zhaw.pages.start | 278 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Peer review (Abstract) | de_CH |
| zhaw.title.proceedings | Proceedings of the NOLTA 2020 Conference | de_CH |
| zhaw.webfeed | Bio-Inspired Methods & Neuromorphic Computing | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen Life Sciences und Facility Management | |
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Gygax, G., Füchslin, R. M., & Ott, T. (2020). Self-organized division of labor in networks of forecasting models for time series with regime switches [Conference paper]. Proceedings of the NOLTA 2020 Conference, 278–281.
Gygax, G., Füchslin, R.M. and Ott, T. (2020) ‘Self-organized division of labor in networks of forecasting models for time series with regime switches’, in Proceedings of the NOLTA 2020 Conference, pp. 278–281.
G. Gygax, R. M. Füchslin, and T. Ott, “Self-organized division of labor in networks of forecasting models for time series with regime switches,” in Proceedings of the NOLTA 2020 Conference, Nov. 2020, pp. 278–281.
GYGAX, Gregory, Rudolf Marcel FÜCHSLIN und Thomas OTT, 2020. Self-organized division of labor in networks of forecasting models for time series with regime switches. In: Proceedings of the NOLTA 2020 Conference. Conference paper. November 2020. S. 278–281
Gygax, Gregory, Rudolf Marcel Füchslin, and Thomas Ott. 2020. “Self-Organized Division of Labor in Networks of Forecasting Models for Time Series with Regime Switches.” Conference paper. In Proceedings of the NOLTA 2020 Conference, 278–81.
Gygax, Gregory, et al. “Self-Organized Division of Labor in Networks of Forecasting Models for Time Series with Regime Switches.” Proceedings of the NOLTA 2020 Conference, 2020, pp. 278–81.
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