function ADABOOST(examples, L, K) returns a weighted-majority hypothesis
inputs: examples, set of N labeled examples (x1, y1),…,(xN,yN)
L, a learning algorithm
K, the number of hypotheses in the ensemble
local variables: w, a vector of N example weights, initially 1 ⁄ N
h, a vector of K hypotheses
z, a vector of K hypothesis weights
for k = 1 to K do
h[k] ← L(examples, w)
error ← 0
for j = 1 to N do
if h[k](xj) ≠ yj then error ← error + w[j]
for j = 1 to N do
if h[k](xj) = yj then w[j] ← w[j] · error ⁄ (1 − error)
w ← NORMALIZE(w)
Z[k] ← log(1 − error) ⁄ error
return WEIGHTED-MAJORITY(h, z)
Figure ?? The ADABOOST variant of the boosting method for ensemble learning. The algorithm generates hypothesis by successively reweighting the training examples. The function WEIGHTED-MAJORITY generates a hypothesis that returns the output value with the highest vote from the hypotheses in h, with votes weighted by z.