Trung Le

Learn More
Over the last decade, the sural flap has been popularized as a suitable alternative to free-tissue transfer for soft-tissue coverage of the lower extremity. However, flap failure rates may be increased especially if patients have certain risk factors, such as age over 40 years, peripheral artery disease, venous insufficiency, diabetes mellitus, and others.(More)
We introduce a new model to deal with imbalanced data sets for novelty detection problems where the normal class of training data set can be majority or minority class. The key idea is to construct an optimal hypersphere such that the inside margin between the surface of this sphere and the normal data and the outside margin between that surface and the(More)
BACKGROUND The Comparative Data Analysis Ontology (CDAO) is an ontology developed, as part of the EvoInfo and EvoIO groups supported by the National Evolutionary Synthesis Center, to provide semantic descriptions of data and transformations commonly found in the domain of phylogenetic analysis. The core concepts of the ontology enable the description of(More)
Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data(More)
Support Vector Machine (SVM) is a very well-known tool for classification and regression problems. Many applications require SVMs with non-linear kernels for accurate classification. Training time complexity for SVMs with non-linear kernels is typically quadratic in the size of the training dataset. In this paper, we depart from the very well-known(More)
Current data description learning methods for novelty detection such as support vector data description and small sphere with large margin construct a spherically shaped boundary around a normal data set to separate this set from abnormal data. The volume of this sphere is minimized to reduce the chance of accepting abnormal data. However those learning(More)
Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classification problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be(More)
One of the most challenging problems in kernel online learning is to bound the model size. Budgeted kernel online learning addresses this issue by bounding the model size to a predefined budget. However, determining an appropriate value for such predefined budget is arduous. In this paper, we propose the Nonparametric Budgeted Stochastic Gradient Descent(More)