document: B-EM : a classifier incorporating bootstrap with EM approach for data mining This paper investigates the problem of augmenting labeled data with unlabeled data to improve classification accuracy . This is significant for many applications such as image classification where obtaining classification labels is expensive , while large unlabeled examples are easily available . We investigate an Expectation Maximization ( EM ) algorithm for learning from labeled and unlabeled data . The reason why unlabeled data boosts learning accuracy is because it provides the information about the joint probability distribution . A theoretical argument shows that the more unlabeled examples are combined in learning , the more accurate the result . We then introduce B-EM algorithm , based on the combination of EM with bootstrap method , to exploit the large unlabeled data while avoiding prohibitive I\/O cost . Experimental results over both synthetic and real data sets that the proposed approach has a satisfactory performance .
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