The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
It has been more than 30 years that statistical learning theory (SLT) has been introduced in the field of machine learning. Its objective is to provide a framework for studying the problem of inference that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. Support Vector Machine, a method based on SLT, then emerged and becoming a widely accepted...
Support vector machine (SVM) is discussed to use for recognizing cucumber leaf diseases in this paper. Considering that it is a small number of samples, a new experimental program has been proposed which takes each spot of leaves as a sample instead of taking each leaf as a sample. In the experiments Radial Basis Function (RBF), polynomial and Sigmoid kernel function were also used to carry out comparative...
The transformer condition estimate model is constructed based on SVM and the parameter for SVM-based classifier is determined by adopting cross validation method. Considering the compactness characteristics of DGA data and combining the own characteristics of SVM, a method of fuzzy C-means clustering to pre-select representative samples is presented. Simulation results show that, the pre-selection...
In this paper, we propose a new multi-classification algorithm based on the non-parallel plane support vector machine (SVM). In the approach, data points of each class are proximal to one of nonparallel planes, and at the same time, are far from the other categories to certain extent. This leads to solve convex quadratic optimization problems which the number is the same as the varieties of category...
The performance and regression precision of weak learners (accuracies should be greater than 0.5) for pattern recognition and forecasting can be upgraded using AdaBoost algorithm. Support vector machine (SVM) is a state-of-the-art learning machines and have been widely used in pattern recognition area since 90's of 20th contrary, however the performance of SVM is not stable and easily influenced due...
As a powerful machine learning approach for pattern recognition problems, support vector machine is known to have good generalization ability. Based on the index system of enterprise's self-fulfillment capability, a new integrated evaluation model is established by using support vector regression method. The method has advantages of accuracy, convenience, reliability and rapidity. The method is illustrated...
In this paper, we first propose a method to transform from LDA to PCA with the discriminative information embedded in a whitening transformation, and then we propose a simple support vector machine formulation to LDA. The results of experiments of face recognition conducted on ORL database show the effectiveness of the proposed method.
In order to improve the recognition rate of a sucker rod's defect and reduce the rapture possibility of the rod, the mixed characters include of wavelet packet energy character and the peak value in the time-domain were used as the input of a recognition network, and artificial neural networks (ANN) and support vector machines (SVM) were used and compared as the recognition network to get the best...
Support vector machine (SVM) is applied for classification in this paper. The SVM operates on the principle of structure risk minimization; hence better generalization ability is guaranteed. This paper discussed the basic principle of the SVM at first, and then we chose SVM classifier with polynomial kernel and the Gaussian radial basis function kernel (RBFSVM) to recognize the cancer samples (benign...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.