Results 281 to 290 of about 326,273 (311)

Epsilon-nonparallel support vector regression

Applied Intelligence, 2019
In this work, a novel method called epsilon-nonparallel support vector regression (e-NPSVR) is proposed. The reasoning behind the nonparallel support vector machine (NPSVM) method for binary classification is extended for predicting numerical outputs.
Miguel Carrasco, Julio López 0001, Sebastián Maldonado 0001   +2 more
openaire   +1 more source

A flexible support vector machine for regression

Neural Computing and Applications, 2011
In this paper, a novel regression algorithm coined flexible support vector regression is proposed. We first model the insensitive zone in classic support vector regression, respectively, by its up- and down-bound functions and then give a kind of generalized parametric insensitive loss function (GPILF).
Xiaobo Chen 0001, Jian Yang 0003, Jun Liang 0004   +2 more
openaire   +1 more source

Modified twin support vector regression

Neurocomputing, 2016
The present study suggest modified twin support vector regression (MTSVR) for data regression. In the MTSVR model, the regression function is determined using a pair of unparalleled up and down bound functions. In any optimization problem, a new term is added to obtain structural information of the input data based on the concept of structural ...
Nafiseh Parastalooi, Ali Amiri 0002, Parisa Aliheidari   +2 more
openaire   +1 more source

Square Penalty Support Vector Regression

2007
Support Vector Regression (SVR) is usually pursued using the Ɛ-insensitive loss function while, alternatively, the initial regression problem can be reduced to a properly defined classification one. In either case, slack variables have to be introduced in practical interesting problems, the usual choice being the consideration of linear penalties for ...
Álvaro Barbero Jiménez, Jorge López Lázaro, José R. Dorronsoro   +2 more
openaire   +1 more source

On Lagrangian twin support vector regression

Neural Computing and Applications, 2012
In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed. Though at the outset the algorithm requires inverse of matrices, it has been shown that they would be obtained by performing matrix subtraction of the identity matrix by a scalar multiple of ...
S. Balasundaram, Muhammad Tanveer 0001
openaire   +1 more source

Active Set Support Vector Regression

IEEE Transactions on Neural Networks, 2004
This paper presents active set support vector regression (ASVR), a new active set strategy to solve a straightforward reformulation of the standard support vector regression problem. This new algorithm is based on the successful ASVM algorithm for classification problems, and consists of solving a finite number of linear equations with a typically ...
David R. Musicant, Alexander Feinberg
openaire   +2 more sources

Support Vector Machines for Survival Regression

2012
In this paper we show how the survival analysis problem can be formulated in terms of support vector regression, starting from a quantile regression perspective. We define an appropriate weighted loss function which takes into account possibly censored observations, and we prove bounds on the estimation error and on the quantile property.
Antonio Eleuteri, Azzam Fouad George Taktak   +1 more
openaire   +1 more source

An overview on twin support vector regression

Neurocomputing, 2022
Huajuan Huang, Xiuxi Wei, Yongquan Zhou   +2 more
exaly  

MBSVR: Multiple birth support vector regression

Information Sciences, 2021
Shifei Ding
exaly  

Adaptive Bayesian support vector regression model for structural reliability analysis

Reliability Engineering and System Safety, 2021
Kai Cheng, Zhenzhou Lu
exaly