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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)
Metric learning is n 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)
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)
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)
Increasing evidence has suggested that dysregulation of microRNAs (miRNAs) could contribute to tumor progression. The miR-34 family is directly transactivated by tumor suppressor p53 which is frequently mutated in various cancers; however, the effect of miR-34a on the ovarian cancer cells remains unclear. The aim of the paper was to study the expression of(More)
Aberrant activation of Notch signaling has an essential role in colorectal cancer (CRC) progression. Amplified in breast cancer 1 (AIB1), also known as steroid receptor coactivator 3 or NCOA3, is a transcriptional coactivator that promotes cancer cell proliferation and invasiveness. However, AIB1 implication in CRC progression through enhancing Notch(More)
Crowdsourcing has been an effective and efficient paradigm for providing labels for large-scale unlabeled data. In the past few years, many methods have been developed for inferring labels from the crowd, but few theoretical analyses have been presented to support this popular human-machine interaction process. In this paper, we theoretically study the(More)