Discriminative information-based nonparallel support vector machine

<span lang="EN-US">The use of support vector regression (SVR) for regression tasks has been on increase over the past few years. Unfortunately, the practical application of SVR for regression task is limited due to its dependence on proper setting of its hyper-parameters and associated kernel parameter. Therefore, it become imperative to device a reliable and fast mechanism of determining the value of these parameters that could guarantee lowest generalization error. This paper presents SVR parameter optimization approaches using African buffalo optimisation (ABO) algorithm, i.e. SVR-ABO. The SVR parameters are optimized by using African buffalo optimisation algorithm. Results obtained from several experiments performed has shown that the proposed ABO algorithm has the capability of determining SVR hyper-parameters which most of time has to be done through estimation.</span>

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“…This section present the theory behind SVR equations as given by [27], [28]. Given a regression problem as (1).…”

Section: Theoretical Concept 21 Support Vector Regression (Svr)mentioning

confidence: 99%

“…Various kernel functions can be found in literature, however, radial basis function has been established to be most appropriate for regression task [4], [27] which is represented as (9):…”

Section: Theoretical Concept 21 Support Vector Regression (Svr)mentioning

confidence: 99%

Determination of support vector regression parameters using African buffalo optimization algorithm

Maijama’a

Yusof

Mohsin

2022

IJEECS

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“…Because of its high learning speed and good generalization performance, it has been widely studied and applied. Many improvements of TSVM have been proposed, such as [2]- [18].…”

Section: Introductionmentioning

confidence: 99%

An Effective Semi-Supervised Multi-Label Least Squares Twin Support Vector Machine

Kang

et al. 2020

IEEE Access

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Multi-label twin support vector machine (MLTSVM), being an effective multi-label classifier based on twin support vector machine (TSVM), has been widely studied and applied due to its excellent classification performance. However, there are some disadvantages in classical MLTSVM: (a) MLTSVM needs to solve a series of quadratic programming problems (QPPs), which makes its learning speed lower. (b) For multi-label learning problems, it is very difficult to obtain all labels of all samples. In fact, the datasets that we can obtain only contain a small amount of labeled samples and a large amount of partially labeled and unlabeled samples. However, MLTSVM can only use expensive labeled samples and ignore cheap unlabeled and partially labeled samples. For the drawbacks, we propose a novel semi-supervised multilabel least squares twin support vector machine, called SS-MLLSTSVM. Firstly, to speed up solving, SS-MLLSTSVM introduces the least squares idea into each sub-classifier of MLTSVM, which makes each subclassifier only need to solve a system of linear equations, instead of one QPP. Secondly, SS-MLLSTSVM can make full use of the geometric information in unlabeled and partially labeled samples by introducing manifold regularization term into each sub-classifier. The experimental results on the benchmark datasets show that, compared with the existing multi-label classification algorithms, our SS-MLLSTSVM has better classification performance.

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Maneuver Trajectory Identification for Non-cooperative HGVs Using Support Vector Machine and Evidence Theory

Cheng

Yan

Tang

et al. 2021

Lecture Notes in Electrical Engineering

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Discriminative information-based nonparallel support vector machine

Cited by 11 publications

References 29 publications

Determination of support vector regression parameters using African buffalo optimization algorithm

Determination of support vector regression parameters using African buffalo optimization algorithm

An Effective Semi-Supervised Multi-Label Least Squares Twin Support Vector Machine

Maneuver Trajectory Identification for Non-cooperative HGVs Using Support Vector Machine and Evidence Theory

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