6、Using AdaBoost to Minimize Training Error: A Comprehensive Guide

Using AdaBoost to Minimize Training Error: A Comprehensive Guide

1. Introduction

AdaBoost is a powerful algorithm that can minimize the training error, i.e., the number of mistakes on the training set. Even when weak classifiers have error rates close to 50%, AdaBoost can drive the training error down rapidly. Our analysis is based on the assumption of empirical weak learnability, which offers generality and flexibility.

2. A Bound on AdaBoost’s Training Error

2.1 Main Theorem

We start by proving a fundamental bound on AdaBoost’s training error. The theorem provides a bound on the training error in terms of the error rates of the weak hypotheses, without assumptions about the training set or the weak learner.

Let $\gam