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Chapter and Conference Paper
Structure Learning of Probabilistic Relational Models from Incomplete Relational Data
Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational...
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Chapter and Conference Paper
Analyzing Co-training Style Algorithms
Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each other. In this paper, we present ...
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Chapter and Conference Paper
Distributional Features for Text Categorization
In previous research of text categorization, a word is usually described by features which express that whether the word appears in the document or how frequently the word appears. Although these features are ...
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Chapter and Conference Paper
Exploiting Unlabeled Data in Content-Based Image Retrieval
In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image retrieval (Cbir), is proposed. This approach ...
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Chapter and Conference Paper
Ensembles of Multi-instance Learners
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzing two famous multi-instance lear...