Gene function prediction using labeled and unlabeled data

Learning from Labeled and Unlabeled Data using Graph Min uts

Using Labeled and Unlabeled Data to Learn Drifting Concepts

For many learning tasks, where data is collected over an extended period of time, one has to cope two problems. The distribution underlying the data is likely to change and only little labeled training data is available at each point in time. A typical example is information filtering, i. e. the adaptive classification of documents with respect to a particular user interest. Both the interest o...

Learning from Labeled and Unlabeled Data Using Random Walks

We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classif...

Boosting Statistical Word Alignment Using Labeled and Unlabeled Data

This paper proposes a semi-supervised boosting approach to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. The proposed approach modifies the supervised boosting algorithm to a semisupervised learning algorithm by incorporating the unlabeled data. In this algorithm, we build a word aligner by using both the labeled data and the unlabeled data. T...

Learning from Labeled and Unlabeled Data using Graph Mincuts