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pro vyhledávání: '"Allerbo, Oskar"'
Autor:
Allerbo, Oskar
Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the parameters. The solution can be obtained either as a closed-form solution, which includes solving a system of linear equati
Externí odkaz:
http://arxiv.org/abs/2311.01762
Autor:
Allerbo, Oskar
Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the model parameters. Here, we introduce an equivalent formulation of the objective function of KRR, which opens up both for re
Externí odkaz:
http://arxiv.org/abs/2306.16838
Autor:
Allerbo, Oskar, Jörnsten, Rebecka
Most machine learning methods require tuning of hyper-parameters. For kernel ridge regression with the Gaussian kernel, the hyper-parameter is the bandwidth. The bandwidth specifies the length scale of the kernel and has to be carefully selected to o
Externí odkaz:
http://arxiv.org/abs/2205.11956
Publikováno v:
Journal of Machine Learning Research 24 (2023) 1-53
The elastic net combines lasso and ridge regression to fuse the sparsity property of lasso with the grouping property of ridge regression. The connections between ridge regression and gradient descent and between lasso and forward stagewise regressio
Externí odkaz:
http://arxiv.org/abs/2202.02146
Autor:
Allerbo, Oskar, Jörnsten, Rebecka
Publikováno v:
Journal of Machine Learning Research 22 (2021) 1-28
High-dimensional data sets are often analyzed and explored via the construction of a latent low-dimensional space which enables convenient visualization and efficient predictive modeling or clustering. For complex data structures, linear dimensionali
Externí odkaz:
http://arxiv.org/abs/2102.10873
Autor:
Allerbo, Oskar, Jörnsten, Rebecka
Publikováno v:
Computational Statistics, 2022, 1-19
Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propo
Externí odkaz:
http://arxiv.org/abs/2012.11369
Akademický článek
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Autor:
Allerbo, Oskar, Jörnsten, Rebecka
Kernel ridge regression, KRR, is a non-linear generalization of linear ridge regression. Here, we introduce an equivalent formulation of the objective function of KRR, opening up both for using other penalties than the ridge penalty and for studying
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93edc9e9907de5e6cdc44b7ce826463d
Akademický článek
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Publikováno v:
In Colloids and Surfaces B: Biointerfaces 2011 82(2):632-636
