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What is Classifier Error Rates and Weights Analysis about?

This document describes the AdaBoost algorithm for classification. It initializes all training points with equal weight, then iterates the following steps: 1. Calculate error rates for different classification hypotheses (h) and pick the one with the smallest error rate. 2. Update the weights of training points by increasing the weights of incorrectly classified points and decreasing correctly classified points. 3. Normalize the weights so they sum to 1. It stops when the error rate is small or there are

Author

Zugar Herrera

Language

EN