How are the automatic values of hyper-parameters in Matlab Regression Learner determined?

1 vue (au cours des 30 derniers jours)
Using Matlab regression learner one can choose the auto option for the values of the various hyper-parameters such as epsilon and Kernel scale mode in SVM's. In this case is stated that if auto is chosen the app uses a heuristic procedure to select the kernel scale. Also the same applies in the Gaussian Processes. When Kernel scale mode is set to Auto, it is stated that the app uses a heuristic procedure to select the initial kernel parameters. -What is the heuristic procedure followed? -Are the values given optimised? -If they are why the "tips" encourage the user to give values manualy?
  2 commentaires
Bernhard Suhm
Bernhard Suhm le 4 Août 2018
Are you just trying to understand what's going on, or do you have evidence it's not working as designed?
Georgios Etsias
Georgios Etsias le 5 Août 2018
It is important to know if the selected parameters are the optimal ones or I should do an optimization of my own!

Connectez-vous pour commenter.

Réponses (1)

Ilya
Ilya le 6 Août 2018
If you type
edit classreg.learning.svmutils.optimalKernelScale
in your MATLAB session and hit Return, the editor will bring up the code for that heuristic procedure.
You won't know if these parameters are optimal or not without doing optimization. These are based on a guess. The guess is often good but it can fail from time to time.

Produits


Version

R2018a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by