Multi-task proximal support vector machine

Li, Ya; Tian, Xinmei; Song, Mingli; Tao, Dacheng

doi:10.1016/j.patcog.2015.01.014

Cited by 43 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the effectiveness of the proposed multitask learning models, we compared them to SVR, LS-SVR, MTPSVR [23], and MTLS-SVR [24]. Experiments were performed in MATLAB R2016a on a PC with an Intel Core i5-2500 CPU (3.30 GHz) and 8 GB of RAM.…”

Section: Methodsmentioning

confidence: 99%

“…Gao [22] adopted clustered multitask support vector regression (MT-SVR) to perform age estimation based on facial images, which required the solution of large-scale quadratic programming problems. Li et al [23] proposed multitask proximal support vector machine (MTPSVR), which incurred a lower computational cost than MT-SVR. Xu et al [24] applied multitask least squares support vector machine (MTLS-SVM) to analyze three components of broomcorn samples.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

Zhang

et al. 2022

Axioms

View full text Add to dashboard Cite

Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation among subtasks and achieve a satisfactory learning effect by training all tasks simultaneously. With this motivation, we assume that the related tasks are close enough to share a common model whereas having their own independent models. Based on this hypothesis, we propose a multitask learning least squares support vector regression (MTL-LS-SVR) algorithm, and an extension, EMTL-LS-SVR. Theoretical analysis shows that these models can be converted to linear systems. A Krylov-Cholesky algorithm is introduced to determine the optimal solutions of the models. We tested the proposed models by applying them to forecasts of the Chinese stock market index trend and the stock prices of five stated-owned banks. The experimental results demonstrate their validity.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

Zhang

et al. 2022

Axioms

View full text Add to dashboard Cite

show abstract

“…Additional three benchmark datasets, often used to evaluate MTL methods, are utilized to analyze the properties of the model in Section 5.2. The STL methods used in experiments include SVM, TWSVM, ν-TWSVM, LSTSVM and NPSVM, the MTL methods consist of DMTSVM, MTPSVM [31], MTLS-TSVM [32]…”

Section: Numerical Experimentsmentioning

confidence: 99%

Multi-task nonparallel support vector machine for classification

Liu¹,

Xu²

2022

Preprint

View full text Add to dashboard Cite

Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the dual problems, so it costs much running time. Moreover, kernel trick cannot be directly utilized in the nonlinear case. To effectively avoid above problems, a novel multi-task nonparallel support vector machine (MTNPSVM) including linear and nonlinear cases is proposed in this paper. By introducing -insensitive loss instead of square loss in DMTSVM, MTNPSVM effectively avoids matrix inversion operation and takes full advantage of the kernel trick. Theoretical implication of the model is further discussed. To further improve the computational efficiency, the alternating direction method of multipliers (ADMM) is employed when solving the dual problem. The computational complexity and convergence of the algorithm are provided. In addition, the property and sensitivity of the parameter in model are further explored. The experimental results on fifteen benchmark datasets and twelve image datasets demonstrate the validity of MTNPSVM in comparison with the state-of-the-art algorithms. Finally, it is applied to real Chinese Wine dataset, and also verifies its effectiveness.

show abstract

“…Xu et al [44] proposed a multitask least-squares SVM based on a least-squares SVM [45]. Li et al [46] proposed a multitask proximal SVM based on a proximal SVM [47]. Xie and Sun [48] proposed a multitask centroid twin SVM based on a twin SVM [49].…”

Section: B Multitask Learning Strategymentioning

confidence: 99%

A Novel Synthetic CT Generation Method Using Multitask Maximum Entropy Clustering

Jiang¹,

Xue

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Due to the risk of radiation from computed tomography (CT) scanning on the human body, the number of CT scans that can be performed on an individual each year is limited. However, CT images play a very important role in medical diagnosis. Therefore, this study proposes a method of generating synthetic CT to solve this problem. Considering that magnetic resonance imaging (MRI) is not harmful to the human body, there is no limit on the number of scans that can be performed with this procedure. In this paper, an image segmentation method is used to segment an MRI, and each segment is given a corresponding Hounsfield Unit (HU) value to finally generate a synthetic CT image. Since the image segmentation performance directly affects the generated synthetic CT image, this paper introduces a multitask learning strategy into a maximum entropy clustering (MEC) algorithm. A multitask maximum entropy clustering (MT-MEC) algorithm is proposed, which is used to effectively segment the MRI of the brain. The algorithm can use knowledge from multiple tasks to improve the learning ability of all tasks, and the MEC algorithm can effectively avoid interference from noise. The experimental results show that the proposed MT-MEC algorithm has good image segmentation performance, which results in reliable performance of the final synthetic CT image.INDEX TERMS Synthetic CT, brain MRI, multitask learning, MEC algorithm.

show abstract

Multi-task proximal support vector machine

Cited by 43 publications

References 36 publications

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast

Multi-task nonparallel support vector machine for classification

A Novel Synthetic CT Generation Method Using Multitask Maximum Entropy Clustering

Contact Info

Product

Resources

About