Boosting is a Machine Learning technique that combines several weak classifers with the goal of improving the overall accuracy. In each iteration, the algorithm updates the example weights and builds an additional classifer. A simple voting scheme is used to combine the classifers. The most famous Boosting-based algorithm is AdaBoost. This algorithm increases the weights of the examples that were misclassifed by the previous classifers. Thus, it focuses the additional classifer on the hardest examples. Initially, an uniform weight distribution is assigned to the examples. However, there is no guarantee that this is the best choice for the initial distribution. In this work, we present Boosting at Start (BAS), a new Machine Learning approach based on Boosting. BAS generalizes AdaBoost by allowing the use of an arbitrary initial distribution. We present schemes for the determination of such distribution. We also show how to adapt BAS to Semi-supervised learning schemes. Additionally, we describe the application of BAS in different problems of data and text classifcation, comparing its performance with the original AdaBoost algorithm and some state-of-the-art algorithms for such tasks. The experimental results indicate that a simple modelling using the BAS algorithm generates effective classifers.