Training Algorithms for Supervised Machine Learning: Comparative Study

Abstract

Supervised machine learning is an important task for learning artificial neural networks; therefore a demand for selected supervised learning algorithms such as back propagation algorithm, decision tree learning algorithm and perceptron algorithm has been arise in order to perform the learning stage of the artificial neural networks. In this paper; a comparative study has been presented for the aforementioned algorithms to evaluate their performance within a range of specific parameters such as speed of learning, overfitting avoidance, and their accuracy. Besides these parameters we have included their benefits and limitations to unveil their hidden features and provide more details regarding their performance. We have found the decision tree algorithm is the best as compared with other algorithms that can solve the complex problems with a remarkable speed.