A HYBRID APPROACH OF ACTIVE LEARNING USING SVM AND N-GRAM TECHNIQUE

Active learning is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Learning based on association rules, also called rule based learning, is a technique that uses association rules learning and taking proper decision. Heterogeneous DTC employs a novel data structure, association rule, to compactly store and efficiently retrieve a large number of rules for learning. We consider an active learning scenario in which the supervisor (trainer) can make decisions regarding the possibility to choose new examples for learning. In the classical forms of supervised learning, the training set is chosen according to some known or random given distribution. The supervisor is a passive agent in the sense that he is not able to interact with the training set in order to improve the performances of the learning process. In this paper hybrid approach that use Ngram with SVM has been proposed. Hybrid approach lead formal manner to learn “difficult learning” and “easy learning” related to the training data set.