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Today, Peer-to-peer (P2P) traffic is the most important network flow on the Internet; meanwhile it gives rise to many security problems for the network management. Therefore P2P traffic identification is the hottest topic of P2P traffic management. Support vector machine (SVM) has advantages with resolving small samples for P2P classification problems. However, the performance of SVM is primarily...
Texture is the vital feature for remote sensing image classification, however, it is hard to be described and recognized by computer vision. As a result, lots of approaches have been presented to identify texture image. Among these methods, support vector machine (SVM) is the most successfully used one, which takes advantages of avoiding local optimum, conquering dimension disaster with small samples...
Among the classification algorithms in machine learning, the KNN (K nearest neighbor) algorithm is one of the most frequent used methods for its characteristics of simplicity and efficiency. Even though KNN algorithm is very effective in many situations while it still has two shortcomings, not only is the efficiency of this classification algorithm obviously affected by redundant dimensional features,...
Least squares support vector machine (LS-SVM) has been successfully applied in many classification and regression tasks. The main drawback of the LS-SVM algorithm is the lack of sparseness. Combing the primal least squares twin support vector machine (LS-TSVM) and the sparse LS-SVM with L0-norm minimization, a new sparse least squares support vector regression algorithm with L0-norm in primal space(L...
With the aim of a better generalizing performance than standard SVMs by assigning a fuzzy membership degree to each input point in the classification problem, Fuzzy Support Vector Machines (FSVMs) have been widely applied and have shown great success in various applications. However, the success of such a machine learning technique is inherently limited when applied to the problem of class imbalance...
In K-means clustering algorithm, the selection of cluster number k and initial K-means center has certain influence on the result. It would generate very different aggregation result when confronting with some certain types of data set. This paper aims at proposing an estimation method to evaluate the initial parameters for K-means algorithm. The estimation is executed through data analysis, which...
The analysis of the seafloor in shallow waters using remote sensing imagery at very high spatial resolution is a very challenging topic due to the minimum signal level received; the presence of noisy contributions from the atmosphere, solar reflection, foam, turbidity and water column; and the limited spectral information available for the classification at such depths that impedes, for example, the...
Recently a few works of semi-supervised learning methods based on graph have been proposed for remote sensing. The common idea of these methods are that they build a graph using the samples of the image. Most of their time complexity is relatively large, and they ignore the spatial information of the image, which leads to unsatisfactory classification results. this paper proposes a novel semi-supervised...
Support vector machine (SVM) is a popular classifier dealing with small-scale datasets. It has outstanding performance compared to other classifiers. However the execution time is extremely long when training Big Data. The Graphics Processing Unit (GPU) is a massively parallel device which performs very well as a co-processor. NVIDIA proposed a programming platform, CUDA, in 2006, which makes it much...
In this paper, two contributions are made. Firstly, we propose a discriminating multiple kernel learning (DMKL) algorithm to solve the combination coefficient of basic kernels by maximizing the separability in the kernel Hilbert space in the process of MKL. The core idea of the proposed algorithm is to find the optimal projective direction, which projects the basic kernels to a discriminating kernel,...
This paper investigated approaches using Isomap to extract nonlinear intrinsic features for K-Nearest Neighbor (KNN) to classify Hyperspectral data, and proposed a new classification method, Isomap-based kernel-KNN (IKNN), based on the kernel function of Isomap and kernelized-KNN classifier. This method takes advantage of the global manifold learning ability of Isomap algorithm directly, without explicitly...
The One Class Auto Associative Neural Network (AANN) has been investigated for solving various problems. Nonetheless, it is sensitive to the presence of outliers in the training set, which is known problem for one-class classifiers. For this, attempts have been done via proposing the use of efficient kernel and ensemble method to reduce the effect of outliers for one class support vector machine classifier...
Due to the presence of speckle, the target recognition algorithm of SAR image is different from other algorithms. There exists nuances in the detail of type recognition. This paper proposes Multi-feature fusion classifier of KPFD. Based on data from MSTAR database, the results of experiment show the new algorithm is more effective than other 5 kinds of recognition algorithm and recently recognition...
In this paper, an Arabic recommender system based on opinion analysis and polarity detection is proposed. Unfortunately, working with Arabic adds more difficulties than the other languages, because it implies the solving of different types of problems such as the diversity of dialects, Al hamza, the ambiguity, etc. These sorts of applications produce data with a large number of features, while the...
Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed...
Support Vector Machines is a very attractive and useful tool for classification and regression; however, since they rely on subtle and complex algebraic notions of optimization theory, lose their elegance and simplicity when implementation is concerned. It has been shown that the SVM solution, for the case of separate classes, corresponds to the minimum distance between the respective convex hulls...
The paper takes the automatic detection of pedestrians from a vehicle into consideration using an automotive night vision system. Accidents involving pedestrians at night cause a significant part of deaths in the roads. Therefore, the authors present a design of the automotive night vision system that combines the passive solutions, i.e. those using thermal vision, and active, which use the near infrared...
The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being...
Correct identification of person from a distance is an important issue in the field of visual surveillance and monitoring applications. To identify the person while their walking, Gait play an important role. During their walking, every parts of the human body move differently. Which part of the body, contributes more for person identification, on the basis of this, we have developed rectangular region...
The identification and classification of noise sources in the ocean has become a key task of modern underwater acoustic signal processing and because of the ever changing and complicated oceanic environment, underwater target classification has become a demanding task. An underwater acoustic target classification system identifies the acoustic target from the characteristic acoustic signature. The...
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