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We propose a sparse probabilistic learning approach for nonlinear channel equalization in wireless communication systems, by using the relevant vector machine (RVM) technique. In particular, we propose two versions of the RVM based equalizer: 1) maximum a posterior RVM (MAP-RVM), 2) marginalized RVM (MRVM). Compared to the standard support vector machine (SVM) method, the proposed RVM approach not...
The phenomenal success of social networking sites, such as Facebook, Twitter and LinkedIn, has revolutionized the way people communicate. This paradigm has attracted the attention of researchers that wish to study the corresponding social and technological problems. Link recommendation is a critical task that not only helps increase the linkage inside the network and also improves the user experience...
We propose a web clustering method using social bookmarking data with dimension reduction regarding similarity. To realize this idea we construct the similarity matrix between web pages based on their cooccurrence frequency. Since the similarity matrix includes various kind of noise, we map the similarity matrix onto lower dimension feature space to reduce the noise. Especially we carry out dimension...
Solving complex convection-diffusion equations is very important to many practical mathematical and physical problems. After the finite difference discretization, most of the time for equations solution is spent on sparse linear equation solvers. In this paper, our goal is to solve 2D Nonlinear Unsteady Convection-Diffusion Equations by accelerating an iterative algorithm named Jacobi-preconditioned...
We construct Kernel Co-occurrence Matrices (KCMs) to represent the target model and the target candidates. Then those matrices are employed as the tracking cues in mean shift framework. Some improvements are presented in the implementation of the algorithm. First, the angle relation between pixel-pairs is redefined to depict the asymmetric characteristic of the object. Second, the KCMs of the target...
Sampled vector fields generally appear as measurements of real phenomena. They can be obtained by the use of a particle image velocimetry acquisition device, or as the result of a physical simulation, such as a fluid flow simulation, among many examples. This paper proposes to formulate the unstructured vector field reconstruction and approximation through Machine-Learning. The machine learns from...
Multiscale approaches have been largely considered in several signal processing applications. They play an important role when designing automatic methods to cope with real world measurements where, in most of the cases, there is no prior information about which would be the appropriate scale. The basic idea behind a multiscale analysis is to embed the original signal into a family of derived signals,...
Support vector machine is an algorithm based on structural risk minimization, which has good generalization performance. In the course of multi-sensor information fusion of industrial control, sensor has bigger nonlinearity and fuzzy relation between coefficient and relevant parameter. This paper proposed an algorithm of multi-sensor information fusion based on support vector machine, which offered...
This paper presents a new SVM algorithm framework optimized by PSO algorithm. The value of parameters in the SVM has great influence on the performance of regression model. In previous works the choice of these parameters mainly depends on the experience. In our work PSO algorithm was used to optimize these parameters to form a new SVM framework - PSVM. The proposed algorithm was used to forecast...
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