ML IALGO LINREG: Difference between revisions
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Description: This tag determines with which algorithm to solve the system of linear equations in the ridge regression method for machine learning. | Description: This tag determines with which algorithm to solve the system of linear equations in the ridge regression method for machine learning. | ||
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In the ridge regression method for machine learning one needs to solve for the unknown weights <math>\mathbf{w}</math> within the following equations | |||
<math> | |||
\mathbf{Y} = \mathbf{\Phi} \mathbf{w}. | |||
</math> | |||
The following options are available: | The following options are available: |
Revision as of 18:34, 12 October 2021
ML_IALGO_LINREG = [integer]
Default: ML_IALGO_LINREG = 1
Description: This tag determines with which algorithm to solve the system of linear equations in the ridge regression method for machine learning.
In the ridge regression method for machine learning one needs to solve for the unknown weights within the following equations
The following options are available:
- ML_IALGO_LINREG=1: Bayesian linear regression (see here). Usable with NSW1.
- ML_IALGO_LINREG=2: QR factorization. Usable with NSW=0,1.
- ML_IALGO_LINREG=3: Singular value decomposition. Usable with NSW=0,1.
- ML_IALGO_LINREG=4: Singular value decomposition with Tikhonov regularization. Usable with NSW=0,1.
Related Tags and Sections
ML_LMLFF, ML_W1, ML_WTOTEN, ML_WTIFOR, ML_WTSIF, ML_ISTART