As early as the 1950s, the quest for computer systems that can learn has been a vision of those involved in the field of artificial intelligence and machine learning. However, it was not until the advent in the 1980s of new algorithms coupled with increased computing power that the vision of machine learning was realized via symbolic classifiers such as decision trees, neural networks, and genetic algorithms. A new generation of researchers began to develop machine learning algorithms such as C4.5 and CART for the classification of arbitrary classes of objects on real-world problems such as segmentation and prediction.