Results 31 to 40 of about 875,908 (286)

Nonstationary regression with support vector machines [PDF]

, 2014
In this work, we introduce a method for data analysis in nonstationary environments: time-adaptive support vector regression (TA-SVR). The proposed approach extends a previous development that was limited to classification problems. Focusing our study on
Granitto, Pablo Miguel, Grinblat, Guillermo Luis, Uzal, Lucas César, Verdes, Pablo Fabian   +3 more
core   +2 more sources

Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration. [PDF]

, 2012
Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of ...
Jiang, Xiaoqian, Kim, Jihoon, Menon, Aditya, Ohno-Machado, Lucila, Wang, Shuang   +4 more
core   +2 more sources

Ship Track Regression Based on Support Vector Machine

IEEE Access, 2017
This work was supported in part by the National Natural Science Foundation of China under Grant 61473331, in part by the Natural Science Foundation of Guangdong Province of China under Grant 2014A030307049, in part by the Ordinary University Innovation ...
Bo Ban, Junjie Yang, Pengguang Chen, Jianbin Xiong, Qinruo Wang   +4 more
doaj   +1 more source

Convex support vector regression

European Journal of Operational Research
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers.
Dai, Sheng, Kuosmanen, Timo, Liao, Zhiqiang   +3 more
openaire   +6 more sources

Interpretable support vector regression

Artificial Intelligence Research, 2012
This paper deals with transforming Support vector regression (SVR) models into fuzzy systems (FIS). It is highlighted that trained support vector based models can be used for the construction of fuzzy rule-based regression models. However, the transformed support vector model does not automatically result in an interpretable fuzzy model.
Tamás Kenesei, János Abonyi
openaire   +2 more sources

Least Absolute Deviation Support Vector Regression [PDF]

Mathematical Problems in Engineering, 2014
Least squares support vector machine (LS‐SVM) is a powerful tool for pattern classification and regression estimation. However, LS‐SVM is sensitive to large noises and outliers since it employs the squared loss function. To solve the problem, in this paper, we propose an absolute deviation loss function to reduce the effects of outliers and derive a ...
Wang, Kuaini, Zhang, Jingjing, Chen, Yanyan, Zhong, Ping   +3 more
openaire   +1 more source

Complex Support Vector Machines for Regression and Quaternary Classification

, 2014
The paper presents a new framework for complex Support Vector Regression as well as Support Vector Machines for quaternary classification. The method exploits the notion of widely linear estimation to model the input-out relation for complex-valued data ...
Bouboulis, Pantelis, Dalla, Leoni, Mavroforakis, Charalampos, Theodoridis, Sergios   +3 more
core   +1 more source

Novel Feature Selection Method for Nonlinear Support Vector Regression

Complexity, 2022
The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS-NSVR, which first attempts to solve ...
Kejia Xu, Ying Xu, Yafen Ye, Weijie Chen
doaj   +1 more source

Application of support vector regression in circle detection

Journal of Hebei University of Science and Technology, 2018
Circle detection is one of the most basic and important tasks in machine vision. In order to accurately determine the circle location in complex background images, a new joint algorithm that combines the model of support vector regression with the three ...
Guanmao WU, Linggang CHEN, Qianqian WANG
doaj   +1 more source

Generator Fault Diagnosis with Bit-Coding Support Vector Regression Algorithm

Energies, 2023
Generator fault diagnosis has a great impact on power networks. With the coupling effects, some uncertain factors, and all the complexities of generator design, fault diagnosis is difficult using any theoretical analysis or mathematical model. This paper
Whei-Min Lin
doaj   +1 more source