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Semi-supervised learning and active learning are important techniques to build more accurate model while labeled data are scarce. The objective of this paper is combining both to effectively relieve user labor for multi-class annotation. We propose a novel graph-based active semi-supervised learning framework which aim at efficiently learning a multi-class model with minimal human labor. In particular,...
Semi-supervised learning methods can largely leverage the image annotation problem using both labeled and unlabeled data, especially when the labeled information is quite limited. However, most of them suffer the expensive computation stemming from the batch learning on large training dataset. In this paper we proposed a highly efficient semi-supervised annotation approach with the partial label propagation...
Unstructured database serves as a new database targeted mainly at unstructured data management, which addresses the limitations of relational database. In this paper, an unstructured database management system named Advanced Unstructured Data Repository (AUDR) is introduced. AUDR is designed to manage massive and various types of unstructured data including text, image, audio and video. Based on a...
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