Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data

Machine Learning with Labeled and Unlabeled Data

The field of semi-supervised learning has been expanding rapidly in the past few years, with a sheer increase in the number of related publications. In this paper we present the SSL problem in contrast with supervised and unsupervised learning. In addition, we propose a taxonomy with which we categorize many existing approaches described in the literature based on their underlying framework, da...

Learning with labeled and unlabeled data

In this paper, on the one hand, we aim to give a review on literature dealing with the problem of supervised learning aided by additional unlabeled data. On the other hand, being a part of the author’s first year PhD report, the paper serves as a frame to bundle related work by the author as well as numerous suggestions for potential future work. Therefore, this work contains more speculative a...

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...

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...