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Supervised learning implies use of a program that can learn to classify a given set of labeled examples. These examples are made up of the same number of features. Different feature space is therefore used to represent each example. The learning process is called supervised, as labeled examples are used by the program to take right decision. Thus supervised learning approach requires preparing labeled training data to construct a statistical model, but it is unable toachieve a good performance without a large amount of training data. This is due to data sparseness problem arise if small amount of training data is used. In recent years several statistical methods based on supervised learning method were proposed.
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