Classification of unlabeled online media

Learning Classification with Unlabeled Data

One of the advantages of supervised learning is that the final error metric is available during training. For classifiers, the algorithm can directly reduce the number of misclassifications on the training set. Unfortunately, when modeling human learning or constructing classifiers for autonomous robots, supervisory labels are often not available or too expensive. In this paper we show that we ...

Text Classification from Positive and Unlabeled Examples

Automatic Webpage Classification Enhanced by Unlabeled Data

This paper describes a novel method for webpage classification that uses unlabeled data. The proposed method is based on a sequential learning of the classifiers which are trained on a small number of labeled data and then augmented by a large number of unlabeled data. By taking advantage of unlabeled data, the effective number of labeled data needed is significantly reduced and the classificat...

Positive Unlabeled Learning for Data Stream Classification

Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data i...

Positive Unlabeled Leaning for Time Series Classification

In many real-world applications of the time series classification problem, not only could the negative training instances be missing, the number of positive instances available for learning may also be rather limited. This has motivated the development of new classification algorithms that can learn from a small set P of labeled seed positive instances augmented with a set U of unlabeled instan...