A fast and robust model selection algorithm for multi-input multi-output support vector machine

Mao, Wentao; Xu, Jiucheng; Wang, Chuan; Dong, Longlei

doi:10.1016/j.neucom.2013.01.058

Cited by 34 publications

(14 citation statements)

References 22 publications

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“…In 7 out of the 12 DA problems, our algorithm has an advantage over the others; it improves the average accuracy significantly in domain adaption. The results acquired from a SVM [32] classifier in the unsupervised DA case are shown in Table 2. Our method has better performance compared to the others in terms of 10 DA problems.…”

Section: Resultsmentioning

confidence: 99%

Domain Adaption Based on ELM Autoencoder

Zhang

2017

Mathematical Problems in Engineering

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We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space. By leveraging nonlinear approximation ability and efficient one-pass learning ability of ELM-AE, the proposed domain adaption algorithm can efficiently seek a better cross-domain feature representation than linear feature representation approaches such as PCA to improve domain adaption performance. The widely experimental results on Office/Caltech-256 datasets show that the proposed algorithm can achieve better classification accuracy than PCA subspace alignment algorithm and other state-of-the-art domain adaption algorithms in most cases.

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Section: Resultsmentioning

confidence: 99%

Domain Adaption Based on ELM Autoencoder

Zhang

2017

Mathematical Problems in Engineering

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“…a hyper-spherical insensitive zone, which handles all the outputs together'. Some studies [41][42][43][44][45][46] have proved that MSVR can improve generalization performance of decision model especially when only scarce samples are available. In the absence of adequate complex mechanical design cases, a common phenomenon in most industry companies, MSVR is an ideal option.…”

Section: Msvr-sw-based Adaptationmentioning

confidence: 99%

Multivariable case-based reason adaptation based on multiple-output support vector regression with similarity-related weight for parametric mechanical design

2018

Advances in Mechanical Engineering

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Using historical cases' solutions to obtain feasible solution for new problem is fundamentally to successfully applying case-based reason technique in parametric mechanical design. As a well-known intelligent algorithm, the formulation of support vector regression has been taken for case-based reason adaptation, but the standard support vector regression can only be used as a univariate adaptation method because of its single-output structure, which would result in the ignorance of the possible interrelations among solution outputs. To handle the complicated case adaptation task with large number of problem inputs and solution outputs more efficiently, this study investigates the possibility of multivariable case-based reason adaptation with multiple output by applying multiple-output support vector regression. Furthermore, inspired by the fact that training sample which contains two closer cases can provide more useful information than others, this study adds the similarity-related weight into multiple-output support vector regression and gives high weights to the information provided by such useful training sample during multi-dimensional regression estimation. The superiority of proposed multiple-output support vector regression with similarity-related weight is validated by the actual design example and quantitative comparisons with other adaptation methods. The comparative results indicate that multiple-output support vector regression with similarity-related weight achieves the best performance for largequantity case-based reason adaptation because of its higher accuracy and relatively lower cost.

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“…To overcome this issue, various MIMO-SVM techniques have been proposed to meet this demand. [35][36][37][38] In this article, a MTLS-SVM 35,36 is employed to build up a single surrogate model which can approximate multiple LSFs.…”

Section: The Least Square Mimo-svmmentioning

confidence: 99%

“…As an alternative to the traditional SVMs, multipleinput multiple-output SVMs (MIMO-SVMs) [35][36][37][38] are good choice since they can model the multiple-input multiple-output relationship between the input random variables and the multiple failure modes in a structural system and have been applied in many engineering and science fields. The newly developed multiple-task leastsquares SVMs (MTLS-SVMs) which is proposed by Xu et al 35,36 aims for establishing a surrogate model between input parameters and multiple outputs for multiple classification and regression problems.…”

Section: Introductionmentioning

confidence: 99%

Structural reliability analysis of multiple limit state functions using multi-input multi-output support vector machine

Zhao

Tee

2016

Advances in Mechanical Engineering

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Selecting and using an appropriate structural reliability method is critical for the success of structural reliability analysis and reliability-based design optimization. However, most of existing structural reliability methods are developed and designed for a single limit state function and few methods can be used to simultaneously handle multiple limit state functions in a structural system when the failure probability of each limit state function is of interest, for example, in a reliability-based design optimization loop. This article presents a new method for structural reliability analysis with multiple limit state functions using support vector machine technique. A sole support vector machine surrogate model for all limit state functions is constructed by a multi-input multi-output support vector machine algorithm. Furthermore, this multi-input multi-output support vector machine surrogate model for all limit state functions is only trained from one data set with one calculation process, instead of constructing a series of standard support vector machine models which has one output only. Combining the multi-input multi-output support vector machine surrogate model with direct Monte Carlo simulation, the failure probability of the structural system as well as the failure probability of each limit state function corresponding to a failure mode in the structural system can be estimated. Two examples are used to demonstrate the accuracy and efficiency of the presented method.

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A fast and robust model selection algorithm for multi-input multi-output support vector machine

Cited by 34 publications

References 22 publications

Domain Adaption Based on ELM Autoencoder

Domain Adaption Based on ELM Autoencoder

Multivariable case-based reason adaptation based on multiple-output support vector regression with similarity-related weight for parametric mechanical design

Structural reliability analysis of multiple limit state functions using multi-input multi-output support vector machine

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