Adaboost

Abstract

Let’s now look at the AdaBoost setup in more detail. • Loss ` = exp (exponential loss). It is also common to use the logistic loss ln(1 + exp(·)), but for simplicity we’ll use the standard choice. • Examples ((xi, yi))i=1 with xi ∈ X and yi ∈ {−1,+1}. The main thing to note is that X is just some opaque set, we are not assuming vector space structure, and can not form inner products 〈w, x〉. • Elementary hypotheses H = (hj)j=1, where hj : X → [−1,+1] for each j. Rather than interacting with examples in X directly, boosting algorithms embed them in a vector space via these functions H. For example, a vector v ∈ R is now interpreted as a linear combination of elements of H, and predictions on a new example x ∈ X are computed as x 7→ ∑