A Novel Sparse Least Squares Support Vector Machines

Xia, Xiao-Lei; Jiao, Weidong; Li, Kang; Irwin, G.W.

doi:10.1155/2013/602341

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…In addition, in studies conducted earlier, the only indicator of the quality of the classification was the reliability of the classification. It is desirable to evaluate other classification indicators, in particular, the proportion of erroneous decisions on the quality of the shot based on the parameters of the acoustic field based on the classifier by the Least Squares Support Vector Machine (LSSVM) [28,29]. This method requires a smaller training sample and has a higher performance.…”

Section: The Study Materials and Methodsmentioning

confidence: 99%

“…As a result, the LSSVM method reduces the estimated time by almost an order of magnitude with the same classification quality indicators. In addition, the use of the LSSVM method makes it possible to reduce the volume of the training sample by 2-4 times [41]. In addition, there is a well-established and verified toolbox from the MATLAB system for this method [42].…”

Section: Procedures For Categorizing the Effectiveness Of A Single Shot From A Gunmentioning

confidence: 99%

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Devising a method for improving the efficiency of artillery shooting based on the Markov model

Boltenkov

Brunetkin

Dobrynin

et al. 2021

EEJET

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This paper reports a method for improving the firing efficiency of an artillery unit that results in enhanced effectiveness. Given the modern use of artillery for counter-battery warfare, the effectiveness of shooting is not enough assessed by accuracy only. It is also necessary to take into consideration and minimize the time spent by the unit in the firing position and the consumption of shells to hit the target. It has been shown that in order to assess the effectiveness of an artillery shot due to the initial velocity of the projectile, the most rapid and simple means is to classify the quality of the shot by the acoustic field. A procedure for categorizing the shot has been improved by applying an automatic classifier with training based on a machine of support vectors with the least squares. It is established that the error in the classification of the effectiveness of the second shot does not exceed 0.05. The concept of the effectiveness of a single artillery shot was introduced. Under the conditions of intense shooting, there may be accidental disturbances in each shot due to the wear of the charging chamber of the gun, its barrel, and incomplete information about the powder charge. When firing involves disturbances, the firing of an artillery unit can be described by a model of a discrete Markov chain. Based on the Markov model, a method for improving the efficiency of artillery fire has been devised. The method is based on the identification of guns that produce ineffective shots. The fire control phase of the unit has been introduced. In the process of controlling the fire of the unit, such guns are excluded from further firing. A generalized criterion for the effectiveness of artillery firing of a unit, based on the convolution of criteria, has been introduced. It is shown that the devised method significantly improves the effectiveness of shooting according to the generalized criterion.

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Section: The Study Materials and Methodsmentioning

confidence: 99%

Section: Procedures For Categorizing the Effectiveness Of A Single Shot From A Gunmentioning

confidence: 99%

Devising a method for improving the efficiency of artillery shooting based on the Markov model

Boltenkov

Brunetkin

Dobrynin

et al. 2021

EEJET

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show abstract

“…An alternative solution to sparsity improvement of LS-SVM is the application of the Kernel Matching Pursuit (KMP) algorithm. Since the KMP algorithm have been described in detail in [23,24], the paper revisited the idea of the KMP from the perspective of QR decomposition.…”

Section: Kernel Matching Pursuit (Kmp) For Sparsity Improvementmentioning

confidence: 99%

“…The Kernel matching pursuit (KMP) algorithm constructed the solution in a greed manner and at each iteration, selected the sample which led to the maximal drop in the sum of squared error loss [23,16]. Because the KMP algorithm facilitates a direct control of the sparsity of the solution, it has been applied to ease the non-sparseness of the LS-SVM [24]. KMP was closely related to the orthogonal least squares (OLS) method in the field of nonlinear model identification [4].…”

Section: Introductionmentioning

confidence: 99%

Training sparse least squares support vector machines by the QR decomposition

Xia

2018

Neural Networks

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The solution of an LS-SVM has suffered from the problem of non-sparseness. The paper proposed to apply the KMP algorithm, with the number of support vectors as the regularization parameter, to tackle the non-sparseness problem of LS-SVMs. The idea of the kernel matching pursuit (KMP) algorithm was first revisited from the perspective of the QR decomposition of the kernel matrix on the training set. Strategies are further developed to select those support vectors which minimize the leave-one-out cross validation error of the resultant sparse LS-SVM model. It is demonstrated that the LOOCV of the sparse LS-SVM can be computed accurately and efficiently. Experimental results on benchmark datasets showed that, compared to the SVM and variants sparse LS-SVM models, the proposed sparse LS-SVM models developed upon KMP algorithms maintained comparable performance in terms of both accuracy and sparsity.

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“…In view of the sparseness of LS-SVM, a fixed-size LS-SVM was proposed to realize the sparse representation in primal weight space [3]. Upon adding a bias term to the objective function, LS-SVM was solved through forward least squares approximation; thus, a sparse solution is generated in the least square sense [4]. Afterward, a sparse algorithm of least squares support vector regression was established based on Householder transformation [5].…”

Section: Introductionmentioning

confidence: 99%

Parsimonious Wavelet Kernel Extreme Learning Machine

Qin¹,

Shen²,

Yu³

et al. 2015

JESTR

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In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM) was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM). In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM) maximized its capability to capture the essential features in "frequency-rich" signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

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A Novel Sparse Least Squares Support Vector Machines

Cited by 11 publications

References 23 publications

Devising a method for improving the efficiency of artillery shooting based on the Markov model

Devising a method for improving the efficiency of artillery shooting based on the Markov model

Training sparse least squares support vector machines by the QR decomposition

Parsimonious Wavelet Kernel Extreme Learning Machine

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