Video anomaly detection can be used in the transportation domain to identify unusual
patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other …

The generalization error bound of the support vector machine (SVM) depends on the ratio of
the radius and margin. However, conventional SVM only considers the maximization of the …

Distance metric is the basis of many learning algorithms, and its effectiveness usually has a
significant influence on the learning results. In general, measuring distance for numerical …

Distance metric learning has been a hot research spot recently due to its high effectiveness
and efficiency in improving the performance of distance related methods, such as k nearest …

Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …

In this paper, we address the semisupervised distance metric learning problem and its
applications in classification and image retrieval. First, we formulate a semisupervised …

Distance metric learning aims to deal with the data distribution by learning a suitable
distance metric from the training instances. For distance metric learning, the optimization …

This paper develops a coarse-to-fine framework for single-image super-resolution (SR)
reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the …

In this paper we propose a frameworks for identifying patterns and regularities in the pseudo-
anonymized Call Data Records (CDR) pertaining a generic subscriber of a mobile operator …

Distance metric learning aims to learn from the given training data a valid distance metric,
with which the similarity between data samples can be more effectively evaluated for …