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.
Numerous factors may impact a student's ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. In this work we study and analyze the impact of such factors on the graduation rates of a university using three predictive models: Support Vector Machines (SVMs), Gaussian Processes (GPs) and Deep Boltzmann Machines...
Support vector machines (SVM) is a widely used method which can treat problems involving small sample, devilish learning, and high dimension. The current paper conduct a multivariate SVM in a total-factor production framework, and the GDP per capita, capital stock and labor are taken as the independent variables and the energy consumption is the dependent variable. The Gaussian radial basis function...
Aimed at the research on freeway detection algorithm has great significance for improving efficiency and effectiveness of freeway traffic management, this paper based on the freeway traffic flow's characteristics, in accordance with the incident detection's basic principle, researches on freeway incident detection based on Support Vector Machine (SVM). This paper designs four different simulation...
The problem of time validity of biometric models has received only a marginal attention from researchers. Actual and up-to-date at the time of their creation, extracted features and models relevant to a person's face may eventually become outdated, leading to a failure in the face identification task. If physical characteristics of the individual change over time, their classification model has to...
This paper analyzes the impact of different detrending approaches on the performance of a variety of computational intelligence (CI) models. Three approaches are compared: Linear, nonlinear detrending (based on empirical mode decomposition) and first-differencing. Five representative CI methods are evaluated: Dynamic evolving neural-fuzzy inference system (DENFIS), Gaussian process (GP), multilayer...
Rockburst is a geological disaster occurred usually in deep mines. Because of poor understanding of the mechanism and influence factors of rockburst, it is very difficult to give accurate prediction using conventional methods. A new model based on Gaussian process (GP), which is a probabilistic kernel machine leaning and has become a power tool for solving highly nonlinear problems, therefore, is...
Patch-based approaches have become popular in many computer vision applications over recent years. An intrinsic flaw of this framework, missing of the spatial information, however, restricts its usage in face related applications where the spatial configuration is relatively settled. In this paper, we introduce a new patch feature representation, namely spatial Gaussian mixture models (SGMM), which...
This paper proposes a classification approach that incorporates the statistical methods GMM and support vector machines. The proposed GMM-SVM system is presented and experimentally evaluated on text independent speaker identification. Our results prove that the combination approach GMM-SVM is significantly superior than SVM approach. We report improvements of 85,37% amelioration in identification...
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.