AdaBoost vs. BayesianBoosting
Hi!
I'm goint o try some boosting for my bachelor's thesis. I haven't yet decided whether using AdaBoost or BayesianBoosting. Actually I don't understand all the differences. What I do understand is that BayesianBoosing can use different fractions of the example set for model fitting and performance estimation. I understand that it is able to reweight examples to ensure equally distributed labels. But what exactly means [tt]allow_marginal_skews[/tt]?
Martin Scholz (the author of the operator) cites in the help text Scholz/2005b. Can anyone give me publication details on (t)his work. I think I would understand the differencen in detail if i read it.
Best regards,
chero
I'm goint o try some boosting for my bachelor's thesis. I haven't yet decided whether using AdaBoost or BayesianBoosting. Actually I don't understand all the differences. What I do understand is that BayesianBoosing can use different fractions of the example set for model fitting and performance estimation. I understand that it is able to reweight examples to ensure equally distributed labels. But what exactly means [tt]allow_marginal_skews[/tt]?
Martin Scholz (the author of the operator) cites in the help text Scholz/2005b. Can anyone give me publication details on (t)his work. I think I would understand the differencen in detail if i read it.
Best regards,
chero