As a crucial issue in computer network security, anomaly detection is receiving more and more attention from both application and theoretical point of view. In this paper, a novel anomaly detection scheme is proposed. It can detect anomaly network traffic which has extreme large value on some original feature by the major component, or does not follow the correlation structure of normal traffic...

Canonical correlation analysis (CCA) is a method for finding linear relations between two multidimensional random variables. This paper presents a generalization of the method to more than two variables. The approach is highly scalable, since it scales linearly with respect to the number of training examples and number of views (standard CCA implementations yield cubic complexity). The method i...

In this paper, a host-vector model is considered for a disease without immunity in which the current density of infectious vectors is related to the number of infectious hosts at earlier times. Spatial spread in a region is modelled in the partial integro-differential equation by a diffusion term. For the general model, we first study the stability of the steady states using the contracting-con...

Discriminative correlation filters (DCF) have recently shown excellent performance in visual object tracking area. In this paper we summarize the methods of updating model filter from discriminative correlation filter (DCF) based tracking algorithms and analyzes similarities and differences among these methods. We deduce the relationship among updating coefficient in high dimension (kernel tric...

This paper presents a new algorithm for classifying distributions. The algorithm combines the principle of margin maximization and a kernel trick, applied to distributions. Thus, it combines the discriminative power of support vector machines and the well-developed framework of generative models. It can be applied to a number of real-life tasks which include data represented as distributions. T...

Based on the study of a generalized form of representer theorem and a specific trick in constructing kernels, a generic learning model is proposed and applied to support vector machines. An algorithm is obtained which naturally generalizes the bias term of SVM. Unlike the solution of standard SVM which consists of a linear expansion of kernel functions and a bias term, the generalized algorithm...

Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. Multiview learning is concerned about learning from multiple distinct feature sets, which aims to exploit distinct views to improve generalization performance. In this paper...

We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligandbased virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor g (PPARg) [1]. PPARg is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods incl...

In this article, we present an efficient algorithm for solving a class of chance-constrained optimization under nonparametric uncertainty. Our is built on the possibility representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use foundation to formulate one minimizing distance between desired distribution and constraint RKHS. provide systematic way cons...

In this paper, we address the received signal strength (RSS)-based indoor localization problem in a wireless local area network (WLAN) environment and formulate it as a multi-class classification problem using survey locations as classes. We present a discriminatively regularized least square classifier (DRLSC)-based localization algorithm that is aimed at making use of the class label informat...