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- research-articleOctober 2024
A safe screening rule with bi-level optimization of ν support vector machine
AbstractSupport vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem. As a famous extension of the traditional SVM, the ν support vector machine (ν-SVM) has shown outstanding performance due to its ...
Highlights- A Safe screening rule and the DCDM algorithm are proposed.
- A bi-level optimization structure could guarantee the computational cost.
- The safe screening is first considered in Unsupervised task and constraints with parameters.
- research-articleFebruary 2024
Matrix-based vs. vector-based linear discriminant analysis: A comparison of regularized variants on multivariate time series data
Information Sciences: an International Journal (ISCI), Volume 654, Issue Chttps://doi.org/10.1016/j.ins.2023.119872AbstractOver the past two decades, matrix-based or bilinear discriminant analysis (BLDA) methods have received much attention. However, it has been reported that the traditional vector-based regularized LDA (RLDA) is still quite competitive and could ...
Highlights- A new regularized BLDA (RBLDA) is proposed for multivariate time series (MTS) classification.
- An efficient model selection algorithm is developed for the proposed RBLDA.
- Empirical results show that the proposed RBLDA generally ...
- ArticleDecember 2022
Deep Twin Support Vector Networks
AbstractTwin support vector machine (TSVM) is a successful improvement for traditional support vector machine (SVM) for binary classification. However, it is still a shallow model and has many limitations on prediction performance and computational ...
- research-articleDecember 2018
A safe sample screening rule for Laplacian twin parametric-margin support vector machine
Pattern Recognition (PATT), Volume 84, Issue CPages 1–12https://doi.org/10.1016/j.patcog.2018.06.018Highlights- A safe sample screening rule (SSSR-LTPSVM) for the LTPSVM is presented based on variational inequality.
Laplacian support vector machine (SVM) for semi-supervised classification has attracted much attention in recent years. As an extension to improve the computational speed, Laplacian twin parametric-margin SVM (LTPSVM) has shown ...
- articleDecember 2018
Asymmetric ?-twin support vector regression
Neural Computing and Applications (NCAA), Volume 30, Issue 12Pages 3799–3814https://doi.org/10.1007/s00521-017-2966-zTwin support vector regression (TSVR) aims at finding ??-insensitive up- and down-bound functions for the training points by solving a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one as in the conventional SVR. ...
- research-articleJanuary 2018
A safe screening rule for Laplacian support vector machine
Engineering Applications of Artificial Intelligence (EAAI), Volume 67, Issue CPages 309–316https://doi.org/10.1016/j.engappai.2017.10.011Laplacian support vector machine (LapSVM) has received much concern in semi-supervised learning (SSL) field. To further improve its computational speed, many efficient algorithms have been developed. However, they just focus on the method of solving ...
- articleJune 2016
v-twin support vector machine with Universum data for classification
Applied Intelligence (KLU-APIN), Volume 44, Issue 4Pages 956–968https://doi.org/10.1007/s10489-015-0736-0A novel -twin support vector machine with Universum data (U $\mathfrak {U}_{\nu }$-TSVM) is proposed in this paper. U $\mathfrak {U}_{\nu }$-TSVM allows to incorporate the prior knowledge embedded in the unlabeled samples into the supervised learning. ...
- research-articleMarch 2016
A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification
Knowledge-Based Systems (KNBS), Volume 95, Issue CPages 75–85https://doi.org/10.1016/j.knosys.2015.12.005The twin hyper-sphere support vector machine (THSVM) classifies two classes of samples via two hyper-spheres instead of a pair of nonparallel hyper-planes as in the conversional twin support vector machine (TSVM). Moreover THSVM avoids the matrix ...
- research-articleJanuary 2016
Laplacian twin parametric-margin support vector machine for semi-supervised classification
Neurocomputing (NEUROC), Volume 171, Issue CPages 325–334https://doi.org/10.1016/j.neucom.2015.06.056As an extension of twin support vector machine (TSVM), twin parametric-margin support vector machine (TPMSVM) makes the learning speed faster than that of the parametric-margin -support vector machine (par- -SVM), and it is suitable for many cases, ...
- articleApril 2015
Structural least square twin support vector machine for classification
Applied Intelligence (KLU-APIN), Volume 42, Issue 3Pages 527–536https://doi.org/10.1007/s10489-014-0611-4The least square twin support vector machine (LS-TSVM) obtains two non-parallel hyperplanes by directly solving two systems of linear equations instead of two quadratic programming problems (QPPs) as in the conventional twin support vector machine (TSVM)...
- articleMarch 2015
Imbalanced and semi-supervised classification for prognosis of ACLF
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS), Volume 28, Issue 2Pages 737–745Acute-on-chronic liver failure (ACLF) is characterized by jaundice, coagulopathy, hepatic encephalopathy, and associated with high mortality. According to the progress of patients, we partition 81 ACLF patients into three groups. Group I includes 40 ...