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: firstname.lastname@example.org).
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.