Supervised learning in Artificial Neural Network (ANN)

Supervised learning means learning from examples, where a training set is given which acts as examples for the classes. The system finds a description of each class. Once the description has been formulated, it is used to predict the class of previously unseen objects. This is similar to discrimination analysis which occurs in statistics.

Supervised learning deals with learning function from available training data. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. Common examples of supervised learning include-

1. Classify E-mail as spam
2. Labeling webpages based on their content 
3. Voice recognition

There are many examples of supervised learning algorithms like SVMs (support vector machines), Naive Bayes classifiers, neural networks, and decision trees.