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IJMLC 2012 Vol.2(5): 560-563 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.189

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Hadi Hormozi, Elham Hormozi, and Hamed Rahimi Nohooji

Abstract—Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques used in robotic manipulators. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.

Index Terms—Machine learning, adaptive control, repetitive control, robot manipulators.

The authors are with the Department of Computer Science, Islamic Azad University, Buinzahra branch, Buinzahra, Iran(e-mail: hadyhormozy@yahoo.com).


Cite:Hadi Hormozi, Elham Hormozi, and Hamed Rahimi Nohooji, "The Classification of the Applicable Machine Learning Methods in Robot Manipulators," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 560-563, 2012.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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