Algorithm 1: Conventional AdaBoost technique

Input: training dataset $S=\left\{\left(x_1,y_1\right),\dots ,\left(x_2,y_2\right),\dots ,\left(x_n,y_n\right)\right\}$, base learner $h$, the number of training rounds $T$.

Output: the final strong classifier $H$.

Procedure: 1.for $i=1:1:n$ 2.compute the weight of the sample $x_i$: $D_1\left(i\right)=\frac{1}{n}$ 3.end for 4.for $t=1:1:T$ 5.select a training data subset $X$ from $S$, fit $h$ using $X$ to get a weak classifier $h_t$, compute the classification error ${\epsilon }_t$: ${\epsilon }_t=P\left[h_t\left(x_i\right)\ne y_i\right]={\displaystyle \sum }_{i=1}^n{D}_t\left(i\right)\mathsf{{\rm I}}\left[h_t\left(x_i\right)\ne y_i\right]$ where $h_t\left(x_i\right)$ denotes the predicted label of $x_i$ using the weak classifier $h_t$, and $y_i$ denotes the actual label of $x_i$. 6.compute the weight of $h_t: {\alpha }_t=\frac{1}{2}\ln \left(\frac{1-{\epsilon }_t}{{\epsilon }_t}\right)$ 7.update the weight of all the instances in $S$: for $i=1:1:n$ $D_{t+1}\left(i\right)=\frac{D_t\left(i\right)}{Z_t}\exp \left(-{\alpha }_t{y}_i{h}_t\left(x_i\right)\right)$ where $Z_t$ is a normalization factor and is calculated as: $Z_t={\displaystyle \sum }_{i=1}^n{D}_t\left(i\right)\exp \left(-{\alpha }_t{y}_i{h}_t\left(x_i\right)\right)$ 8.end for 9.end for 10.assuming $H\left(x\right)$ is the class label for an instance $x$; after the iterations, the final classifier $H$ is obtained as: $H\left(x\right)=sign\left({\displaystyle \sum }_{t=1}^T{\alpha }_t{h}_t\left(x\right)\right)$