乐胖代购免代理版

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To ensure an up-to-date model delivering useful predictions at all times, model reconfigurations are required to adapt to such evolving streams. For Gaussian processes, this might require the adaptation of the internal kernel expression. In this paper, we present dynamically self-adjusting Gaussian processes by introducingE<\/jats:bold>vent-T<\/jats:bold>riggeredK<\/jats:bold>ernelA<\/jats:bold>djustments in Gaussian process modelling (ETKA), a novel data stream modelling algorithm that can handle evolving and changing data distributions. To this end, we enhance the recently introduced Adjusting Kernel Search with a novel online change point detection method. Our experiments on simulated data with varying change point patterns suggest a broad applicability of ETKA. On real-world data, ETKA outperforms comparison partners that differ regarding the model adjustment and its refitting trigger in nine respective ten out of 14 cases. These results confirm ETKA\u2019s ability to enable a more accurate and, in some settings, also more efficient data stream processing via Gaussian processes.<\/jats:p>","DOI":"10.1007\/978-3-031-15791-2_10","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T07:34:06Z","timestamp":1663313646000},"page":"96-114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamically Self-adjusting Gaussian Processes for\u00a0Data Stream Modelling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4133-6232","authenticated-orcid":false,"given":"Jan David","family":"H\u00fcwel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-376X","authenticated-orcid":false,"given":"Florian","family":"Haselbeck","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2085-4591","authenticated-orcid":false,"given":"Dominik G.","family":"Grimm","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Beecks","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"10_CR1","unstructured":"Adams, R.P., MacKay, D.J.C.: Bayesian online changepoint detection (2007)"},{"key":"10_CR2","doi-asserted-by":"publisher","unstructured":"Alagu Dharshini, M.P., Antelin Vijila, S.: Survey of machine learning and deep learning approaches on sales forecasting. 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