Open AccessTraining Algorithms for Supervised Machine Learning: Comparative Study
Author(s) -
Rafiqul Zaman Khan,
Haider Allamy
Publication year - 2013
Publication title -
international journal of management and information technology
Language(s) - English
Resource type - Journals
ISSN - 2278-5612
DOI - 10.24297/ijmit.v4i3.773
Subject(s) - computer science , machine learning , artificial intelligence , overfitting , decision tree , artificial neural network , algorithm , wake sleep algorithm , online machine learning , multilayer perceptron , instance based learning , supervised learning , learning classifier system , task (project management) , rprop , semi supervised learning , types of artificial neural networks , time delay neural network , generalization error , management , economics
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.