Abstract
Supervised learning involves training using well “labelled” training data, and on the basis of the training data, machines are able to predict the output. The labelled data means that the correct output is attached along with the corresponding input data. In supervised learning, as the name suggests, the training data acts as the supervisor and provides training to the machine to predict the correct output. This chapter discusses Statistical Decision Theory, Gaussian & Normal Distribution, Conditionally Independent Binary Components, Learning Beliefs Network and Nearest-Neighbour Methods.
Keywords: Belief networks, Normal distribution, Statistical decision theory, Supervised learning.
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