Fuzzy and interval-valued fuzzy nonparallel support vector machine

Ju, Hongmei; Hou, Qiuling; Jing, Ling

doi:10.3233/jifs-18702

Cited by 11 publications

(9 citation statements)

References 23 publications

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“…(a) Selection and decision of evaluation indicators: first of all, from its own architectural form, functional positioning, and the urban space around the urban complex, very complex-related influencing factors should be removed, and a wide range of common index influencing factors should be selected in a planned way. Second, the valuable experience of previous successful project cases should be taken into full consideration, and the useful indicators should be evaluated and compared in combination with the engineering quantity of the project itself and the characteristics of its geographical location, so as to clearly describe the characteristics and advantages of urban complex [17]. In combination with the target expectation of the project, this paper selected a reasonable evaluation index after summarizing and analyzing a large number of literatures and collecting relevant useful information about the public space design of multiple urban complexes.…”

Section: Selection Principle and Process Of Evaluation Indicatorsmentioning

confidence: 99%

Urban Complex Public Space Design Method Based on Support Vector Machine

Liu

2022

Mathematical Problems in Engineering

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Urban commercial complex is the product of social and economic development, as well as the inevitable trend of commercial development. The public space design of urban complex project is an important link in its development process, which is the link between the contact and the building, the city and the building, and the external image display of the complex. Therefore, choosing a suitable and excellent public space design scheme is of great significance to show the superiority of the complex project and improve the project satisfaction and the sustainable operation in the later stage. This paper first defines the concept of urban complex public space and focuses on the analysis of the urban complex public space from four aspects of constituent elements, functional classification, design theory, and principle. Then, the Delphi method is used to determine 26 evaluation indicators to evaluate the urban complex public space design scheme, and the relevant index data of 20 urban complex public space design schemes are collected. The grey correlation analysis method is used to analyze the correlation degree between each indicator and the scheme winning bid. The final evaluation system of urban complex public space design scheme is obtained after removing the 8 indicators with low correlation degree. Support vector mechanism was used to build the optimization model of urban complex public space design scheme, and its accuracy was verified, which provides a relatively simple and quantitative method for urban public space design in the future, helps to improve the city’s business structure, improves the business environment of the city, makes the commercial complex more effectively play its own characteristics and advantages, effectively stimulates the vitality of urban space, and creates more perfect architectural space and urban space for people.

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Section: Selection Principle and Process Of Evaluation Indicatorsmentioning

confidence: 99%

Urban Complex Public Space Design Method Based on Support Vector Machine

Liu

2022

Mathematical Problems in Engineering

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“…So, in this proposed method, the performance is better in nonlinear classifiers compared with linear. To analyze the performance of all the methods on several UCI benchmark data sets statistically, as it was suggested in [2,8,17], we assumed Friedman test with the corresponding post hoc tests which are considered to be a simple, nonparametric yet safe test.…”

Section: 22mentioning

confidence: 99%

Smooth augmented Lagrangian method for twin bounded support vector machine

Bazikar

Ketabchi

Moosaei

2022

NACO

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<p style='text-indent:20px;'>In this paper, we propose a method for solving the twin bounded support vector machine (TBSVM) for the binary classification. To do so, we use the augmented Lagrangian (AL) optimization method and smoothing technique, to obtain new unconstrained smooth minimization problems for TBSVM classifiers. At first, the augmented Lagrangian method is recruited to convert TBSVM into unconstrained minimization programming problems called as AL-TBSVM. We attempt to solve the primal programming problems of AL-TBSVM by converting them into smooth unconstrained minimization problems. Then, the smooth reformulations of AL-TBSVM, which we called AL-STBSVM, are solved by the well-known Newton's algorithm. Finally, experimental results on artificial and several University of California Irvine (UCI) benchmark data sets are provided along with the statistical analysis to show the superior performance of our method in terms of classification accuracy and learning speed.</p>

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“…where µ and σ represent the mean and standard deviation of the characteristic parameters x i . Support vector machine (SVM) is a machine learning method proposed by Vapnik et al in the 1990s, used to solve classification and regression problems [22][23][24]. It is a supervised learning algorithm that classifies the target samples according to the distribution information of the current samples.…”

Section: Principle Of Support Vector Machine Algorithmmentioning

confidence: 99%

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

Wang

Zheng

2021

Remote Sensing

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This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter–support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter–support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.

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Fuzzy and interval-valued fuzzy nonparallel support vector machine

Cited by 11 publications

References 23 publications

Urban Complex Public Space Design Method Based on Support Vector Machine

Urban Complex Public Space Design Method Based on Support Vector Machine

Smooth augmented Lagrangian method for twin bounded support vector machine

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

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