One-Class Remote Sensing Classification From Positive and Unlabeled Background Data

Class Prior Estimation from Positive and Unlabeled Data

We consider the problem of learning a classifier using only positive and unlabeled samples. In this setting, it is known that a classifier can be successfully learned if the class prior is available. However, in practice, the class prior is unknown and thus must be estimated from data. In this paper, we propose a new method to estimate the class prior by partially matching the class-conditional...

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...

Text Classification from Positive and Unlabeled Examples

Classification from Pairwise Similarity and Unlabeled Data

One of the biggest bottlenecks in supervised learning is its high labeling cost. To overcome this problem, we propose a new weakly-supervised learning setting called SU classification, where only similar (S) data pairs (two examples belong to the same class) and unlabeled (U) data are needed, instead of fully-supervised data. We show that an unbiased estimator of the classification risk can be ...

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data

Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster assumption. In contrast, recently developed methods of classification from positive and unlabeled data (PU classification) use unlabeled data for risk evaluation, i.e., label information is directly extracted from unla...