Review on Supervised Learning Techniques

In most of the application domains, few tasks can be solved by supervised learning where they can be used to label the data patterns from the existing data. This paper reviews various supervised learning techniques like decision trees, rule-based learners, lazy learners such as NNC, and a comparison of major supervised learning mechanisms like neural networks and support vector machines. The strengths and weakness of unsupervised learning techniques are also compared. This paper reviews about various supervised learning techniques strengths and weakness, brief review of unsupervised techniques, and navigation to semi-supervised learning.