Performance evaluation of kernel functions based on grid search for support vector regression

Ma, Xiuzhen; Zhang, Yanbin; Wang, Yanxia

doi:10.1109/iccis.2015.7274635

Cited by 39 publications

(23 citation statements)

References 12 publications

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“…Various methods have been developed for tuning the SVR hyperparameters. In this study, we used the grid search method, which is one of the most common and reliable techniques for model selection (Ma, Zhang, & Wang, 2015). The range of selective parameters between (2 À15 and2 15 ) were chosen in order to search for the best setting over the parameter space.…”

Section: Parameter Selection Of Svrmentioning

confidence: 99%

Retrieval of soil salinity from Sentinel-2 multispectral imagery

Taghadosi

Hasanlou

Eftekhari³

2019

European Journal of Remote Sensing

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Soil salinity is a widespread environmental hazard and the main causes of land degradation and desertification, especially in arid and semi-arid regions. The first step in finding such a solution is providing accurate information about the severity and extent of the salinity spread in affected areas; this can be done by mapping the electrical conductivity (EC) of the soil. Utilizing the potential of high-resolution satellite imagery along with remote sensing techniques is a promising method to map salinity, as it allows for large-scale monitoring and provides high accuracy and efficiency. This paper, therefore, aims at assessing soil salinity by mapping the EC of soils, using satellite imagery from the newly launched Sentinel-2 satellite as well as Landsat-8 data. A field study was carried out using those data, and various salt features were extracted that relate the EC values of field samples to satellite-derived salt features. The study used two different regression approaches MLP and SVR. Additionally, two feature selection algorithms, GA and SFS, were implemented on the data to improve model performance. The study concludes that the proposed method for modeling salinity and the mapping of soil EC can be considered an effective approach for soil salinity monitoring.

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Section: Parameter Selection Of Svrmentioning

confidence: 99%

Retrieval of soil salinity from Sentinel-2 multispectral imagery

Taghadosi

Hasanlou

Eftekhari³

2019

European Journal of Remote Sensing

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“…As SVM is a typical two-class classifier, we need to Py to search them [22] . Here γ = 0:004 and C = 0:25.…”

Section: Classifier Establishmentmentioning

confidence: 99%

Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

Ronghui¹,

Zhang²,

Jing³

et al. 2017

International Journal of Agricultural and Biological Engineering

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The hyper-spectral image contains spectral and spatial information, which increases the ability and precision of objects classification. Despite the classification value of hyper-spectral imaging technology within various applications, users often find it difficult to effectively apply in practice because of the effect of light, temperature and wind in outdoor environment. This research presented a new classification model for outdoor farmland objects based on near-infrared (NIR) hyper-spectral images. It involves two steps including region of interest (ROI) acquisition and establishment of classifiers. A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly. Then maximum likelihood (ML) and support vector machine (SVM) were used for farmland objects classification. The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%, of which the SVM-M model could reach 99.5%. The research provided an effective method for outdoor farmland image classification.

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“…Due to the nonlinear mapping capability, the kernel methods have attracted the attention of many researchers in recent years. However, different kernel functions have different characteristics [22]. Choosing different kernel functions is crucial for dealing with different problems [23].…”

Section: Introductionmentioning

confidence: 99%

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Liu

et al. 2020

Sensors

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In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

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Performance evaluation of kernel functions based on grid search for support vector regression

Cited by 39 publications

References 12 publications

Retrieval of soil salinity from Sentinel-2 multispectral imagery

Retrieval of soil salinity from Sentinel-2 multispectral imagery

Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

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