Robust cancer molecular pattern identification from microarray data not only plays an essential role in modern clinic oncology, but also presents a challenge for statistical learning. Although principal component analysis (PCA) is a widely used feature selection algorithm in microarray analysis, its holistic mechanism prevents it from capturing the latent local data structure in the following c...

groundwater contamination by nitrate is a globally growing problem due to the population growth and increase of demand for food supplies.increasing nitrate concentrations in soil solution and leaching into the ground water table cause water pollution and disturbs the ecological balance. in addition to natural nitrogen cycle, nitrate can be entered to soil and water from the human waste, urban a...

Automatic image annotation (AIA) is an active topic of research in computer vision and pattern recognition. In the last two decades, large amount of researches on AIA have been proposed, mainly including classification-based methods and probabilistic modeling methods. As one of the most common methods for AIA, support vector machine (SVM) has been widely applied in the multimedia research commu...

With the availability of genetic pathways or networks and accumulating knowledge on genes with variants predisposing to diseases (disease genes), we propose a disease-gene-centric support vector machine (DGC-SVM) that directly incorporates these two sources of prior information into building microarray-based classifiers for binary classification problems. DGC-SVM aims to detect the genes cluste...

Even the support vector machine (SVM) has been proposed to provide a good generalization performance, the classification result of the practically implemented SVM is often far from the theoretically expected level because their implementations are based on the approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM,...

With the rapid growth of internet finance, the credit assessing is becoming more and more important. An effective classification model will help financial institutions gain more profits and reduce the loss of bad debts. In this paper, we propose a new Support Vector Machine (SVM) based ensemble model (SVM-BRS) to address the issue of credit analysis. The model combines random subspace strategy ...

In this contribution we propose the so-called SVM attack, a profiling based side channel attack, which uses the machine learning algorithm support vector machines (SVM) in order to recover a cryptographic secret. We compare the SVM attack to the template attack by evaluating the number of required traces in the attack phase to achieve a fixed guessing entropy. In order to highlight the benefits...

In this paper we evaluate the performance of the highest probability SVM nearest neighbor (HP-SVM-NN) classifier, which combines the ideas of the SVM and k-NN classifiers, on the task of spam filtering. To classify a sample, the HP-SVM-NN classifier does the following: for each k in a predefined set {k1, ..., kN} it trains an SVM model on k nearest labeled samples, uses this model to classify t...