A Statistical View to Boosting

Speaker:

Time:

Friday, 16 August 2013, 16:00 to 17:30

Venue:

• D-405 (D-Block Seminar Room)

Organisers:

In machine learning, AdaBoost has been an extremely popular boosting algorithm to improve the performance of weak learners". AdaBoost was initially proposed by Schapire and Freund from an algorithmic perspective. The statistical machine learners (who maintain that all machine learning algorithms are derived from a statistical framework) remained  skeptical of AdaBoost until Freidman et.al gave a statistical view of boosting and proved that boosting is equivalent to fitting additive models.

We will study this generalized boosting models and obtain AdaBoost as a specialized version.