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Image super-resolution is the process by which additional information is incorporated to enhance a low resolution image thereby producing a high resolution image. In the simplest case super-resolution of a single image is a process of obtaining high-resolution image with more number of pixels with more resolving power. Therefore the super-resolved image should demonstrate an improvement in the perceived...
In this paper we present a new One-Versus-All or OVA-based scheme for multi-class classification problems, aiming to reduce the training time when applying support vector machines (SVMs), particularly on large datasets. The experimental results on ten benchmark datasets show that the performance of the proposed scheme, referred to as "VINE", is comparable to that of its predecessor OVA scheme,...
With the wide application of computer network, the server is considered as the core of the whole network application. Therefore, the server performance monitoring is becoming more and more important. Through the monitoring about the CPU, memory, and numbers of processes and other parameters of the server, it can judge the server performance is good or bad. However, most alarm systems are based on...
Emerging biomedical sensors and stimulators offer unprecedented modalities for delivering therapy and acquiring physiological signals (e.g., deep brain stimulators). Exploiting these in intelligent, closed-loop systems requires detecting specific physiological states using very low power (i.e., 1–10mW for wearable devices, 10–100µW for implantable devices). Machine learning is a powerful tool for...
A novel computer network intrusion detection approach based on the relevance vector machine (RVM) classification is proposed, where a Chebyshev chaotic map is introduced as the inner training noise signal. According to the known distribution property of the Chebyshev map, the iteration process of RVM classifier can be derived and be realized easily. Compared with the support vector machine (SVM) classification...
The classification of land use in karst areas is mainly through the interpretation of satellite images to get. The traditional interpretation methods are supervised classification and unsupervised classification. But the classification polygons is trivial by supervised classification, and boundary is also complex. Different categories can be distincted by unsupervised classification, however, the...
Support vector machines (SVMs) have been used in a variety of classification tasks. SVMs undoubtedly are one of the most effective classifiers in several data mining applications. Determination of a kernel function and related parameters has been a bottleneck for this group of classifiers. In this paper a novel approach is proposed to use genetic programming (GP) to design domain-specific and optimal...
The classification of remotely sensed images knows a large progress seen the availability of images of different resolutions as well as the abundance of the techniques of classification. Moreover a number of works showed promising results by the fusion of spatial and spectral information. For this purpose we propose a methodology allowing to combine this two information to refine an SVM classification,...
In view of the aero engine sensor fault phenomena, combined with sparse support vector machines and robustness, aero engine sensor fault diagnosis is designed by using SVM. SVM is trained out of line, and used on line. Compared the output results with the actual system output, it can produce high precision fault residuals by the simulation system's dynamic characteristics which was having been trained...
Correct and timely diagnosis of diseases is an essential matter in medical field. Limited human capability and limitations decrease the rate of correct diagnosis. Machine learning algorithms such as support vector machine (SVM) can help physicians to diagnose more correctly. In this study, Wisconsin diagnostic breast cancer (WDBC) data set is used to classify tumors as benign and malignant. Independent...
The dimensionality disaster problem exist in pattern recognition process, the fault diagnosis method on nonlinear feature kernel extraction is presented here. The fisher linear discriminant analysis method is extended to nonlinear fields by kernel technology, the original feature space is mapped into observation space for features linearization, The fault pattern is classified by fisher linear discriminant...
In this paper, a new classification scheme of fully polarimetric SAR images is proposed. This is based on the joint use of the Freeman-Durden decomposition and generalized discriminant analysis, a new method for Feature extraction. After getting the powers of the three scattering mechanism components through Freeman-Durden decomposition, the Feature extraction algorithm is introduced to well exploit...
For the problem of standard support vector machines do not provide posteriori probability that needed in many uncertain classification problems, a modeling method of probability support vector machines based on cross entropy is proposed, and the method of determining model parameters is given in detail. on this base, the multi-calss support vector machines probability model is built and the probability...
New diagnosis method of induction motor faults based on classification of the current waveforms is presented in this paper. This method is composed of two sequential processes: a feature extraction and a rule decision. The diagnosis is realized the detection of different faults — bearing fault, stator fault and rotor fault. K-nearest neighbor (K-NN) is used as decision criterion. The flexibility of...
The discovery of DNA microarray technologies have given immense opportunity to make gene expression profiles for different cancer types. Besides binary classification such as normal versus tumor samples the discrimination of multiple tumor types is also important. In this work, we have first extended the recently developed binary nonparallel plane proximal classifier (NPPC) to multiclass NPPC by decomposition...
Using the Mallat fast algorithm with sym5 wavelet, the pulse waves of 20 heroin druggers and 20 healthy normal subjects are decomposed into two levels. The squared distances from the third and tenth scale coefficients in the second-level decomposition of every pulse wave to the global mean value are used to form a feature vector. The extracted feature vectors have good separable characteristics in...
This paper investigates the effect of optimizing Support Vector Machine, with linear and RBF kernels, on its performance in classifying asphyxiated infant cries, with Orthogonal Least Square. Mel Frequency Cepstrum analysis first extracts feature from the infant cry signals. The extracted features are then ranked in accordance to its error reduction ratio with OLS. SVM with linear and RBF kernel then...
The support vector data description (SVDD) is a method proposed to solve the problem of one-class classification. It models a hypersphere around the target set, and by the introduction of kernel functions, more flexible descriptions are obtained. In SVDD, the width parameter s and the penalty parameter c have to be given beforehand by the user. To automatically optimize the values for these parameters,...
We introduce a facial expression recognition method, which incorporates a weight to the Local Binary Pattern (LBP), and generates solid expression features. Furthermore, we use Adaboost to select a small set of prominent features, which is used by the Support Vector Machine (SVM) to classify facial expressions efficiently. Experimental results demonstrate that our method outperforms the state-of-the-art...
A set of new mental tasks that can be used with Brain Computer Interface systems are introduced. New mental tasks are natural to perform for controlling a cursor on a computer screen to select icons. With the help of three healthy subjects, performance of new mental tasks was evaluated and compared with that of popular motor imagery mental tasks. Three subjects who participated in this study showed...
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