Tata Institute of Fundamental Research
A Statistical View to Boosting
Student Seminar
Speaker:
Tapan Shah
Organiser:
Anantha Karthik R
Date:
Friday, 16 Aug 2013, 16:00 to 17:30
Venue:
D-405 (D-Block Seminar Room)
(Scan to add to calendar)
Abstract:
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.
| Speaker: | Tapan Shah |
| Organiser: | Anantha Karthik R |
| Date: | Friday, 16 Aug 2013, 16:00 to 17:30 |
| Venue: | D-405 (D-Block Seminar Room) |
(Scan to add to calendar)
Abstract:
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.
We will study this generalized boosting models and obtain AdaBoost as a specialized version.