We propose a method to solve this problem. First, we give each unlabeled examples an initial class label using unsupervised learning. Then, by maximizing the ...

Abstract. Labeled samples are crucial in semi-supervised classification, but which samples should we choose to be the labeled samples? In other words,.

Missing: Classification | Show results with:Classification

Missing: Classification | Show results with:Classification

Abstract. Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers.

Active label query selection is per- formed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information.

Active Learning via Membership Query Synthesis for Semi-supervised Sentence Classification. Raphael Schumann. Institute for Computational Linguistics.

Ac- tive learning is performed on top of the semi- supervised learning scheme by greedily select- ing queries from the unlabeled data to minimize the estimated ...

This work focuses on constraint (also known as query) selection for improving the performance of semi-supervised clustering algorithms, and presents an ...

Jul 15, 2019 · We propose an active semi-supervised learning algorithm with multiple criteria. · The criteria are representativeness, diversity, and variance ...