Learning relies on the acquisition of different types of knowledge supported by perceived information. It leads to the development of new capacities, skills, values, understanding, and preferences. Its goal is the increasing of individual and group experience. The paper starts with introduction of learning. Semi-supervised learning addresses the problem of labeled data by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice. The paper discusses various important approaches to semi-supervised learning such as self-training, co-training(CO). expectation maximization (EM) .CO-EM. Then the use of graph-based methods is explained. Finally the use of combination of support vector machines (SVM) and K-means clustering method is explained. The paper concludes with the proposed adaptive learning methodology. Copyright © 2009 by IICAI.