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Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem in semi-supervised metric learning and proposes a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraint. Our regularizer is designed and developed based on local topology, which is(More)
In semi-supervised classification boosting, a similarity measure is demanded in order to measure the distance between samples (both labeled and unlabeled). However, most of the existing methods employed a simple metric, such as Euclidian distance, which may not be able to truly reflect the actual similarity/distance. This paper presents a novel similarity(More)
At present, China's express market demands and supply capacity of the express service providers is increasingly prominent. In the view of the problems such as scattered, small, bad in the express industry in China, the collaborative mode of service network based on horizontal resource integration is proposed. Through the cooperative operation of the express(More)
Similarity measurement is crucial for unsupervised learning and semi-supervised learning. Unsupervised methods need a similarity to do clustering. Semi-supervised algorithms need a similarity to take advantage of unlabeled data. In this paper, we develop a boosted similarity learning algorithm. Based on the manifold assumption, our similarity is learned(More)
Presenting effective augmenting information is helpful for users to perceive and interact in augmented reality systems. In this paper, a novel method for evaluating the human-computer interface in optical see-through augmented reality system is proposed. The main contribution presented in this paper is a user-based study that adopts the Radius Basis(More)
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