Training Algorithms for Supervised Machine Learning: Comparative Study

Journal Title: INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY - Year 2013, Vol 4, Issue 3

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.

Authors and Affiliations

Dr. Rafiqul Zaman Khan, Haider Allamy

Keywords

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  • EP ID EP654253
  • DOI 10.24297/ijmit.v4i3.773
  • Views 196
  • Downloads 0

How To Cite

Dr. Rafiqul Zaman Khan, Haider Allamy (2013). Training Algorithms for Supervised Machine Learning: Comparative Study. INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY, 4(3), 354-360. https://europub.co.uk/articles/-A-654253