Machine Learning Algorithm : ensemble (part 7 of 12)

In machine learning and computational learning theory, Logit Boost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The original paper casts the AdaBoost algorithm into a statistical framework. Specifically, if one considers AdaBoost as a generalized additive model and then applies the cost functional of logistic regression, one can derive the LogitBoost algorithm. LogitBoost can be seen as a convex optimization. Bootstrap Aggregation (or Bagging for short), is a simple and very powerful ensemble method.