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.
This paper compares different smoothing techniques for graduating fertility rates. In particular we focus on some well-known parametric models, standard non-parametric statistical methods such as kernels and splines, and Support Vector Machines (SVM). In this work, we apply these techniques to empirical age-specific fertility rates from a variety of populations and time periods.
Through statistic analysis of vibration and temperature signals of motor on the container crane hoisting mechanism in Waigaoqiao port, the feature vectors with vibration and temperature are obtained. Through data preprocessing and training data, Training models of condition parameters based on support vector machine (SVM) are established. The testing data of condition monitoring parameters can be...
The Kernel Support Vector Machine (KSVM) is a powerful nonlinear classification methodology where, the Support Vectors (SVs) fully describe the decision surface by incorporating local information in the Kernel space. On the other hand, the Kernel Fisher Discriminant(KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. This paper proposes...
Support Vector Machines (SVM) represents a new and very promising approach to pattern recognition based on small dataset. The approach is systematic and properly motivated by Statistical Learning Theory (SLT). Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of Structural...
Our goal is to fit the multiple instances (or structures) of a generic model existing in data. Here we propose a novel model selection scheme to estimate the number of genuine structures present. In contrast to conventional model selection approaches, our method is driven by kernel-based learning. The input data is first clustered based on their potential to have emerged from the same structure. However...
This paper analyzed some existing problems of the present air-cargo forecast methods. Then it established the SVM (support vector machine) model for air-cargo demand forecasting. Taking the historical statistical data of Beijing to Shanghai cargo volumes from Jan-2005 to Mar-2006 as fitting and forecasting specimens, we can obtain the prediction model to optimize, which was compared with that of Brown...
In order to detect the abnormal status of business process and reduce possible loss, it is necessary to build an outlier detect model. Based on the statistic learning and the support vector classifier theory, a new business processes' outlier detection model is proposed based on the support vector data description. Firstly, the paper discussed the concept of the business process and the abnormal running...
Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of statistical risk evaluation models. Conformal Predictors are a recently developed set of...
In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order to overcome its shortage, an improved interpolation called support vector machine-Kriging interpolation (SVM-Kriging) was proposed in this paper. The SVM-Kriging uses least square support vector machine (LS-SVM)...
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.