Shihai Wang

Learn More
Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into(More)
Unlike traditional pattern classification, semi-supervised learning provides a novel technique to make use of both labeled and unlabeled data for improving the performance of classification. In general, there are two critical issues for semi-supervised learning of discriminative classifiers; i.e., how to create an initial classifier of a good generalization(More)
—Software faults could cause serious system errors and failures, leading to huge economic losses. But currently none of inspection and verification technique is able to find and eliminate all software faults. Software testing is an important way to inspect these faults and raise software reliability, but obviously it is a really expensive job. The(More)
In the machine learning community, most algorithms proposed, particularly for inductive learning, are based entirely on one crucial assumption: that the training and test data points are drawn or generated from the exact same distribution. If this condition is not fully satisfied, most learning algorithms or models are corrupted. In this paper, we propose a(More)
AADL (The architecture analysis and design language) can be used to describe the reliability of the safety critical system. In this paper, we, firstly, make an introduction on the AADL dependability model and summary the basic rules for translating from Error Model Annex (EMA) of AADL to Fault tree, and then we make a series of improvements on the(More)
It is a troublesome issue that when performed on imbalanced data sets, most classification algorithms off the shelf actually do not behave well enough, manifesting itself as minorities which may be more desired to be correctly distinguished are contrarily worse (even dramatically) classified than their counterparts are. This paper gives a theoretical(More)