English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Boosting and other ensemble methods combine a large number of weakclassifiers through weighted voting to produce stronger predictive models. Toexplain the successful performance of boosting algorithms, Schapire et al.(1998) showed that AdaBoost is especially effective at increasing the marginsof the training data. Schapire et al. (1998) also developed an upper bound onthe generalization error of any ensemble based on the margins of the trainingdata, from which it was concluded that larger margins should lead to lowergeneralization error, everything else being equal (sometimes referred to as the``large margins theory''). Tighter bounds have been derived and have reinforcedthe large margins theory hypothesis. For instance, Wang et al. (2011) suggestthat specific margin instances, such as the equilibrium margin, can bettersummarize the margins distribution. These results have led many researchers toconsider direct optimization of the margins to improve ensemble generalizationerror with mixed results. We show that the large margins theory is notsufficient for explaining the performance of voting classifiers. We do this byillustrating how it is possible to improve upon the margin distribution of anensemble solution, while keeping the complexity fixed, yet not improve the testset performance.

On the Insufficiency of the Large Margins Theory in Explaining the Performance of Ensemble Methods