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Based on vector wavelet kernel function, a method for analog circuit diagnosis based on genetic algorithm (GA) and least squares wavelet support vector machine (LSWSVM) is proposed. Using wavelet package as a tool for extracting feature, the GA-LSWSVM is then applied to the filet circuit after training by GA; the simulation results have shown that the method can enhances the accuracy and generalization...
Radar high resolution range profile (HRRP) is sensitive to the target aspect and highly overlapped in feature space between different targets, thereby hybrid features are suitable for representing the target's property. In this paper, the quadratic spline wavelet with compact support properties was used to extract the energy spectrum features of HRRP by multi-resolution decomposition, and the power...
This paper addresses the problem of automatic wavelet feature extraction for signal classication. We propose to jointly learn wavelet-based features (including scale and translation of the wavelet as well as its shape) and a decision function by casting the problem as a Multi-Kernel Learning problem. A novel active constraints algorithm is then proposed. Our method has been tested on a toy dataset...
Support Vector Machines is a supervised classifier which used kernel functions to mitigate nonlinear problem. Various kernel functions like Gaussian and polynomial kernels previously used for hyperspectral image classification. In this paper, new kernel function is used for hyperspectral image classification. This kernel is based on wavelet which named wavelet-kernel. The comparative result of Wavelet...
The occurrence of acute hypotensive episodes (AHE) in intensive care units (ICU) seriously endanger the lives of patients, and are depended mainly on the expert experience of doctors to treat currently. How to detect and predict AHE in advance has become a clinical problem which is highly paid attention to by the medical world. In this paper, the theory of medical Informatics has been applied to achieve...
As we know, Electrocardiogram (ECG) supervising is the most efficient and effective way of preventing Cardiovascular diseases. ECG arrhythmia intelligent analysis system will not only save time but also provide accurate diagnosis for physicians. Recently, we have developed ambulatory ECG (AECG) arrhythmia intelligent analysis software (AIAS) by Visual Basic 6.0 with the total accuracy 95.9%. The purpose...
The large number of methods for EEG feature extraction demands a good choice for EEG features for every task. This paper compares three subsets of features obtained by tracks extraction method, wavelet transform and fractional Fourier transform. Particularly, we compare the performance of each subset in classification tasks using support vector machines and then we select possible combination of features...
A novel algorithm is presented for classification of four patterns of diffuse lung disease: normal, emphysema, honeycombing and ground glass opacity, on the basis of textural analysis of high resolution computed tomography (HRCT) lung images. The algorithm incorporates scale-space features based on Gaussian derivative filters and multi-dimensional multi-scale features based on wavelet and contourlet...
After analyzing the characteristic of ICT (industrial computerized tomography) image and wavelet transformation singularity, an crack edge checking method for coal CT image based on SVM was studied and improved, and applied in crack edge checking of Coal CT image. First we get the direction and approximate bound of the crack using SVM, and then compare the average area grayscale to locate the crack...
SVM (Support Vector Machines) is the most advanced machine learning algorithm in the field of pattern recognition. The selection of kernel functions will have a direct impact on the performance of SVM. This paper analyzed Linear kernel function, Polynomial kernel function, Radial basis function (RBF), Sigmoid kernel function, Fourier kernel function, B-spline kernel function and Wavelet kernel function,...
This paper proposes the application of kernel principal component analysis (KPCA) for power quality (PQ) problem classification. First, the features of PQ signal are extracted using wavelet-multiresolution analysis. Then, KPCA captures the dominant nonlinear properties of the extracted features by transforming to a high dimensional feature space. The dimension of extracted features produced by KPCA...
It is an important method to help doctor's clinical diagnosis that using pattern recognition technology recognizes and counts Urine Sediment's visible component. Harr wavelet feature has good property of distinguish different components, the proposed method using AdaBoost to select a little part typical Harr feature which are taken as input data of SVM. The trained several bi-class SVM classifiers...
A vehicle recognition algorithm is proposed to solve imbalanced datasets in vehicle recognition based on SVM ensembles. Moreover, an improved Wavelet feature algorithm is also presented. Experimental results show that the presented method has high precision and recall. Furthermore, the system performance can also be improved by increasing learning and has better application.
This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classification. This set is based on the well established Gabor feature. A circular sum of the Gabor feature elements belonging to the same scale is proposed to reduce the effect of rotation, while a slide matching of augmented scales is proposed to address the effect of scaling. The resulting feature vector...
Support vector machine (SVM) is a machine learning algorithm, which has been used recently for classification of hyperspectral images. SVM uses various kernel functions like RBF and polynomial to map the data into higher dimensional space to improve data separability. New kernel functions are used in this paper to classify hyperspectral images which are based on wavelet functions as named wavelet-kernels...
This paper used the multi-class classification for support vector machine and combined with the good amplitude-frequency characteristic of Fourier transform,the good time-frequency characteristics of wavelet transform and the excellent statistical learning ability of support vector machine to make the classification and recognition to the disturbances of power quality. Mathematical modeling for the...
This paper describes polynomial kernel subspace approach to speaker recognition systems. Auditory motivated wavelet packet transform is used to derive the desirable speaker features. The nonlinear mapping between the input space and the feature space is implicitly performed using the kernel trick. This nonlinear mapping increases the discrimination capability of a pattern classifier. The use of Mel-scale...
This paper proposes a new approach to characterize texture image at multiresolution using the non-subsampled contourlet transform, a new geometrical multiresolution transform. The support vector machines (SVMs), which have demonstrated excellent performance in a variety of pattern recognition problems, are used as classifiers. The Classification experiments with 20 Brodatz textures indicate that the...
A new rotation invariant texture descriptor based on the difference of offset Gaussian (DooG) and a sub-micro pattern encoding are proposed. We first apply the Gabor wavelet to texture images. We then utilize the DooG to measure the difference between the center positive Gaussian and the neighbor rotated negative one. We encode the local micro texture using our proposed method, a sub-micro pattern...
Image segmentation is a key step in the application of Synthetic Aperture Radar (SAR) images, but because of the existing of speckles in SAR images, image can not be divided well by using traditional methods. According to the remarkable result of wavelet transform on texture feature extraction, image filtration and the advantages of support vector machine (SVM) classification, an efficient method...
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