Temat: IRLS - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Generalized ordered linear regression with regularization
Autorzy:: Łęski, J.
Henzel, N.
Powiązania:: https://bibliotekanauki.pl/articles/201591.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: linear regression
IRLS
OWA
conjugate gradient optimization
robust methods
Opis:: Linear regression analysis has become a fundamental tool in experimental sciences. We propose a new method for parameter estimation in linear models. The 'Generalized Ordered Linear Regression with Regularization' (GOLRR) uses various loss functions (including the o-insensitive ones), ordered weighted averaging of the residuals, and regularization. The algorithm consists in solving a sequence of weighted quadratic minimization problems where the weights used for the next iteration depend not only on the values but also on the order of the model residuals obtained for the current iteration. Such regression problem may be transformed into the iterative reweighted least squares scenario. The conjugate gradient algorithm is used to minimize the proposed criterion function. Finally, numerical examples are given to demonstrate the validity of the method proposed.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 481-489
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Iteratively reweighted least squares classifier and its l2- and l1-regularized Kernel versions
Autorzy:: Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/199904.pdf
Data publikacji:: 2010
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: classifier design
IRLS
conjugate gradient optimization
gradient projection
Kernel matrix
Opis:: This paper introduces a new classifier design method based on regularized iteratively reweighted least squares criterion function. The proposed method uses various approximations of misclassification error, including: linear, sigmoidal, Huber and logarithmic. Using the represented theorem a kernel version of classifier design method is introduced. The conjugate gradient algorithm is used to minimize the proposed criterion function. Furthermore, .1-regularized kernel version of the classifier is introduced. In this case, the gradient projection is used to optimize the criterion function. Finally, an extensive experimental analysis on 14 benchmark datasets is given to demonstrate the validity of the introduced methods.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2010, 58, 1; 171-182
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

Artykuł

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