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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 a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given that the two(More)
In this paper, we present a new analysis on co-training, a representative paradigm of disagreement-based semi-supervised learning methods. In our analysis the co-training process is viewed as a combinative label propagation over two views; this provides a possibility to bring the graph-based and disagreement-based semi-supervised methods into a unified(More)
Metric learning is a fundamental problem in computer vision. Different features and algorithms may tackle a problem from different angles, and thus often provide complementary information. In this paper, we propose a fusion algorithm which outputs enhanced metrics by combining multiple given metrics (similarity measures). Unlike traditional co-training(More)
Co-training is a famous semi-supervised learning paradigm exploiting unlabeled data with two views. Most previous theoretical analyses on co-training are based on the assumption that each of the views is sufficient to correctly predict the label. However, this assumption can hardly be met in real applications due to feature corruption or various feature(More)
In this paper, we tackle the tracking problem from a fusion angle and propose a disagreement-based approach. While most existing fusion-based tracking algorithms work on different features or parts, our approach can be built on top of nearly any existing tracking systems by exploiting their disagreements. In contrast to assuming multi-view features or(More)
Data in the Internet are scattered on different sites indeliberately, and accumulated and updated frequently but not synchronously. It is infeasible to collect all the data together to train a global learner for prediction; even exchanging learners trained on different sites is costly. In this paper, aggregative-learning is proposed. In this paradigm, every(More)
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