Large-margin Distribution Machine-based regression

Rastogi, Reshma; Anand, Pritam; Chandra, Suresh

doi:10.1007/s00521-018-3921-3

Cited by 19 publications

(12 citation statements)

References 18 publications

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“…Recently, Rastogi et al(2020)introduced a margin distribution-based LDMRwhich was on the spiritof the LDM model (Zhang and Zhou 2014). LDMR simultaneously minimizes the   insensitive loss functionand the quadratic loss function.…”

Section: The Ldmr Modelmentioning

confidence: 99%

Wavelet kernel least square twin support vector regression for wind speed prediction

Hazarika

Gupta

Natarajan³

2022

Environ Sci Pollut Res

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Wind energy is a potent yet freely available renewable energy. It is essential to estimate the wind speed (WS)precisely to makeaprecise estimation of wind power at wind power generating stations.Generally, the WS data is non-stationary. Wavelets have the potential to deal with the non-stationarilyindatasets. On the other hand, the prediction ability of primal least square support vector regression (PLSTSVR) has never been tested to best of our knowledge for WS prediction. Hence, in this work, wavelet kernel-based LSTSVR models are proposed for WS prediction. They are Morlet wavelet kernel LSTSVR and Mexican Hat wavelet kernel LSTSVR.HourlyWS data are collected from four different stations namely Chennai, Madurai, Salem and Tirunelveli in Tamil Nadu, India. The performance of the proposed models isevaluated using root mean square, mean absolute, symmetric mean absolute percentage, mean absolute scaled error and R 2 . The results of the proposed models are compared with twin support vector regression (TSVR), PLSTSVR and large-margin distribution machine-based regression (LDMR). Based on the results of the performance indicators, the performance of the proposed models is better when compared to other models.

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Section: The Ldmr Modelmentioning

confidence: 99%

Wavelet kernel least square twin support vector regression for wind speed prediction

Hazarika

Gupta

Natarajan³

2022

Environ Sci Pollut Res

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“…Peng et al [113] implemented the use of interval data to handle interval input-output data (ITSVR). Rastogi et al [127,128] provided a extension of ν-SVR i.e ν-TWSVR and large margin distribution machine based regression that it is in the true spirit of TSVM. Balasundaram and Meena [7] proposed unconstrained TSVR formulation in the primal space (UP-TSVR) which is speed and obtains better generalization than TSVR.…”

Section: Other Improvements On Twin Support Vector Regressionmentioning

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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“…Zhang and Zhou proposed the large margin distribution machine (LDM) [27]. This idea about optimizing the distribution of margin rather than maximizing the minimum margin is widely used in a variety of methods, such as large-margin distribution machine-based regression (LDMR) [28], least square large margin distribution machine-based regression (LSLDMR) [29], and optimal margin distribution machine [30]. In the aforementioned methods, the inclusion of statistical measures introduces additional parameters.…”

Section: Introductionmentioning

confidence: 99%

Quadratic hyper-surface kernel-free large margin distribution machine-based regression and its least-square form

He,

Wang,

Jiang

et al. 2024

Mach. Learn.: Sci. Technol.

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ǫ-Support vector regression (ǫ-SVR) is a powerful machine learning approach that focuses on minimizing the margin, which represents the tolerance range between predicted and actual values. However, recent theoretical studies have highlighted that simply minimizing structural risk does not necessarily result in well margin distribution. Instead, it has been shown that the distribution of margins plays a more crucial role in achieving better generalization performance. Furthermore, the kernel-free technique oﬀers a signiﬁcant advantage as it eﬀectively reduces the overall running time and simpliﬁes the parameter selection process compared to the kernel trick. Based on existing kernel-free regression methods, we present two eﬃcient and robust approaches named quadratic hyper-surface kernel-free large margin distribution machine-based regression(QLDMR) and quadratic hyper-surface kernel-free least squares large margin distribution machine-based regression(QLSLDMR). The QLDMR optimizes the margin distribution by considering both ǫ-insensitive loss and quadratic loss function similar to the large-margin distribution machine-based regression (LDMR). QLSLDMR aims to reduce the cost of the computing process of QLDMR, which transforms inequality constraints into an equality constraint inspired by least squares support vector machines (LSSVR). Both models are combined the spirit of optimal margin distribution with kernel-free technique and after simpliﬁcation are convex so that they can be solved by some classical methods. Experimental results demonstrate the superiority of the optimal margin distribution combined with the kernel-free technique in robustness, generalization, and eﬃciency.

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Large-margin Distribution Machine-based regression

Cited by 19 publications

References 18 publications

Wavelet kernel least square twin support vector regression for wind speed prediction

Wavelet kernel least square twin support vector regression for wind speed prediction

Comprehensive Review On Twin Support Vector Machines

Quadratic hyper-surface kernel-free large margin distribution machine-based regression and its least-square form

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