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pro vyhledávání: '"Glad, Ingrid K."'
Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health (SoH) monitoring on the Batte
Externí odkaz:
http://arxiv.org/abs/2102.08111
Autor:
Brandsæter, Andreas, Glad, Ingrid K.
Publikováno v:
Data Min Knowl Disc (2022)
This paper proposes a novel approach to explain the predictions made by data-driven methods. Since such predictions rely heavily on the data used for training, explanations that convey information about how the training data affects the predictions a
Externí odkaz:
http://arxiv.org/abs/2012.03625
Autor:
Tveten, Martin, Glad, Ingrid K.
When applying principal component analysis (PCA) for dimension reduction, the most varying projections are usually used in order to retain most of the information. For the purpose of anomaly and change detection, however, the least varying projection
Externí odkaz:
http://arxiv.org/abs/1908.02029
Akademický článek
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Autor:
Brandsæter, Andreas, Glad, Ingrid K.
Publikováno v:
Data Mining & Knowledge Discovery; Sep2024, Vol. 38 Issue 5, p2633-2664, 32p
We consider the problems of variable selection and estimation in nonparametric additive regression models for high-dimensional data. In recent years, several methods have been proposed to model nonlinear relationships when the number of covariates ex
Externí odkaz:
http://arxiv.org/abs/1310.1282
Publikováno v:
In Expert Systems With Applications 1 May 2019 121:418-437
Autor:
Bergersen, Linn Cecilie, Ahmed, Ismaïl, Frigessi, Arnoldo, Glad, Ingrid K., Richardson, Sylvia
We propose a new approach to safe variable preselection in high-dimensional penalized regression, such as the lasso. Preselection - to start with a manageable set of covariates - has often been implemented without clear appreciation of its potential
Externí odkaz:
http://arxiv.org/abs/1210.0380
Autor:
Sandve, Geir K., Gundersen, Sveinung, Rydbeck, Halfdan, Glad, Ingrid K., Holden, Lars, Holden, Marit, Liestøl, Knut, Clancy, Trevor, Ferkingstad, Egil, Johansen, Morten, Nygaard, Vegard, Tøstesen, Eivind, Frigessi, Arnoldo, Hovig, Eivind
Publikováno v:
Genome Biology 2010, 11:R121
The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequ
Externí odkaz:
http://arxiv.org/abs/1101.4898
Publikováno v:
Scandinavian Journal of Statistics, 2003 Jun 01. 30(2), 415-427.
Externí odkaz:
https://www.jstor.org/stable/4616772