Generalized elastic net Lp-norm nonparallel support vector machine

Li, Chun‐Na; Ren, Pei-Wei; Shao, Yuan‐Hai; Ye, Ya-Fen; Guo, Yafei

doi:10.1016/j.engappai.2019.103397

Cited by 17 publications

(5 citation statements)

References 26 publications

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“…While the SVM has demonstrated superior performance compared with most other systems, it encounters limitations in dealing with complex data, primarily due to the high computational cost of solving quadratic programming problems (QPPs) and its strong reliance on the selection of kernel functions and their parameters. Notably, the past decade has seen significant advancements aimed at enhancing the accuracy of SVM. , In the SVM model, a nonlinear function is applied to the training data (eq ), enabling the model to estimate the dependent variables based on the independent variables . The equation provided here represents the formula for predicting the output of an SVM model.…”

Section: Methodsmentioning

confidence: 99%

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Gandomzadeh,

Rohani,

Abbaspour-Fard

2023

Ind. Eng. Chem. Res.

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Section: Methodsmentioning

confidence: 99%

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Gandomzadeh,

Rohani,

Abbaspour-Fard

2023

Ind. Eng. Chem. Res.

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“…In this section, we experimentally compare the proposed CPSVM with GEPSVM [2], L1-GEPSVM [16], PCC [14], L1-NPSVM [15], IGEPSVM [13] LpNPSVM [12], and GLpNPSVM [21]. Experiments are conducted on an artificial dataset with outliers, some benchmark datasets [34], and a handwritten digit image dataset.…”

Section: Methodsmentioning

confidence: 99%

“…In fact, the robustness of L 1 -norm has been applied in many machine learning tasks, including feature extraction [18], dimensionality reduction [19], or clustering [20]. Due to its robustness, modi cations of the L 1 -norm that were applied on NSVMs were also extensively studied, for example, L p -norm (p > 0) and generalized elastic network-based nonparallel support vector machines [12,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Capped $L_{1}$ -Norm Proximal Support Vector Machine

Ren

Shao

2022

Mathematical Problems in Engineering

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Compared to the standard support vector machine, the generalized eigenvalue proximal support vector machine coped well with the “Xor” problem. However, it was based on the squared Frobenius norm and hence was sensitive to outliers and noise. To improve the robustness, this paper introduces capped L 1 -norm into the generalized eigenvalue proximal support vector machine, which employs nonsquared L 1 -norm and “capped” operation, and further proposes a novel capped L 1 -norm proximal support vector machine, called CPSVM. Due to the use of capped L 1 -norm, CPSVM can effectively remove extreme outliers and suppress the effect of noise data. CPSVM can also be viewed as a weighted generalized eigenvalue proximal support vector machine and is solved through a series of generalized eigenvalue problems. The experimental results on an artificial dataset, some UCI datasets, and an image dataset demonstrate the effectiveness of CPSVM.

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“…Although SVM has outperformed most other systems, it still has many limitations in dealing with complex data due to its high computational cost of solving QPPs and its performance highly depends upon the choice of kernel functions and its parameters. Many improvements have been made in the last decade to enhance the accuracy of SVM [85,86]. One such critical enhancement was proposed in 2016 called generalized eigenvalue proximal SVM (GEPSVM) [90] that led the foundation of twin SVM (TSVM) later by Jayadeva et al [60,62].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Generalized elastic net Lp-norm nonparallel support vector machine

Cited by 17 publications

References 26 publications

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Capped $L_{1}$ -Norm Proximal Support Vector Machine

Comprehensive Review On Twin Support Vector Machines

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Generalized elastic net Lp-norm nonparallel support vector machine

Cited by 17 publications

References 26 publications

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Future of Ultrasonic Transducers: How Machine Learning Is Driving Innovation

Capped L 1 -Norm Proximal Support Vector Machine

Comprehensive Review On Twin Support Vector Machines

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Product

Resources

About

Capped $L_{1}$ -Norm Proximal Support Vector Machine