68 search results - page 4 / 14 » On the Margin Explanation of Boosting Algorithms |
NIPS
2003
13 years 9 months ago
2003
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an associated simplified nonlinear iterated map and analyze its behavior in lo...
ML
2007
ACM
13 years 7 months ago
2007
ACM
AdaBoost.OC has been shown to be an effective method in boosting “weak” binary classifiers for multi-class learning. It employs the Error-Correcting Output Code (ECOC) method ...
AAAI
1998
13 years 9 months ago
1998
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic princip...
IDA
2002
Springer
13 years 7 months ago
2002
Springer
This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifi...