The consistency of greedy algorithms for multi-category classification
Hong Chen · Journal of Hubei University · 2005
Learning algorithms,in statistical learning theory,can be described as greedy procedures for stagewise minimization of an appropriate cost funcation.Compared with other procedures,greedy algorithms don't depend on the condition number of associated parametric estimation.This advantage is important since many statistical estimation problem are know to be ill-conditioned.In this paper,learning algorithm for solving multi-category classification using convex upper losses is studied.Estimation error for multi-category classification is established.Finally,the consistency of greedy algorithm is proved.