Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application
نویسندگان
چکیده
The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR the decomposition a large convex quadratic programming problem into two small problems. Therefore, not only has advantages fast computation low computational complexity, but also better performance. Classic SVR, assuming that accords with mean zero, variance noise gaussian distribution consider effects noise, some practical applications, such as wind speed forecasting, characteristic more line for heteroscedasticity distribution, therefore, return existing technology best. In this paper, characteristics Gaussian are introduced model TSVR, based on (TSVR-HGN) constructed. Lagrange multiplier method used solve problem, optimization algorithm global optimization. artificial data set, UCI set were selected experimental comparison. results show TSVR-HGN prediction accuracy.
منابع مشابه
Noise model based v-support vector regression with its application to short-term wind speed forecasting
Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussia...
متن کاملReduced twin support vector regression
Wepropose the reduced twin support vector regressor (RTSVR) that uses the notion of rectangular kernels to obtain significant improvements in execution time over the twin support vector regressor (TSVR), thus facilitating its application to larger sized datasets. & 2011 Elsevier B.V. All rights reserved.
متن کاملOn the Noise Model of Support Vector Machines Regression
Support Vector Machines Regression (SVMR) is a learning technique where the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called the -Insensitive Loss Function (ILF), which is similar to loss functions used in the field of robust statistics. The quadratic loss function is well justified under the assumption of Gaus...
متن کاملVariational Heteroscedastic Gaussian Process Regression
Standard Gaussian processes (GPs) model observations’ noise as constant throughout input space. This is often a too restrictive assumption, but one that is needed for GP inference to be tractable. In this work we present a non-standard variational approximation that allows accurate inference in heteroscedastic GPs (i.e., under inputdependent noise conditions). Computational cost is roughly twic...
متن کاملA weighted twin support vector regression
Twin support vector regression (TSVR) is a new regression algorithm, which aims at finding -insensitive upand down-bound functions for the training points. In order to do so, one needs to resolve a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one in a classical SVR. However, the same penalties are given to the samples in TSVR. In fact, samples in the di...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3215155