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.
Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural...
In this paper, the problem of adaptive channel equalization is considered. We extend the previous work in a new direction with periodic training sequence and formulate the original channel equalization problem into the equivalent linear regression in which ϵ-support vector regression machines are proposed. Slack variables are introduced in our approach to solve the problem of quadratic programming...
Comparing with traditional statistical modeling methods, support vector machine (SVM) has much advantage for solving regression and classification problems. For nonlinear regression, the kernel function of SVM transforms the nonlinear input space into a high dimensional feature space in which the solution of the problem can be represented as being a linear regression problem. Therefore, in all probability...
The forecast of air passenger flow plays an important role in the management of airline, but the traditional forecast methods can't guarantee the generalization capability when they face a large-scale, multi-dimension, nonlinear and non-normal distribution time series data. To improve the forecast ability of air passenger flow, the SVM regression algorithm is introduced in this paper. By selecting...
We propose an algorithm for generating diagnostic rules for cardiac diagnoses. Diagnostic rules are presented in decision tree forms that are created by genetic programming. The algorithm was tested by using cardiac single proton emission computed tomography images. In comparisons with other six well-known methods including support vector machine, LogitBoost, logistic regression, linear discriminant...
In this paper a novel linear regression model is proposed to mine related queries from query logs. Three types of association relationships between queries are identified and leveraged in our model, which include query session co-occurence, URL-clicked sharing and text similarity. Previous work directly applies part of these relations, which may be largely affected by the noise in query logs, such...
In this paper, we propose a novel nonlinear ensemble rainfall forecasting model integrating generalized linear regression with artificial neural networks (ANNs). In this model, using different linear regression extract linear characteristics of rainfall system. Then using different ANNs algorithms and different network architecture extract nonlinear characteristics of rainfall system. Thirdly, the...
A key component in most parametric classifiers is the estimation of an inverse covariance matrix. In hyperspectral images, the number of bands can be in the hundreds, leading to covariance matrices having tens of thousands of elements. Lately, the use of linear regression in estimating the inverse covariance matrix has been introduced in the time-series literature. This paper adopts and expands these...
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.