High-Low Level Support Vector Regression Prediction Approach (HL-SVR) for Data Modeling with Input Parameters of Unequal Sample Sizes
نویسندگان
چکیده
Support vector regression (SVR) has been widely used to reduce the high computational cost of computer simulation. SVR assumes input parameters have equal sample sizes, but unequal sizes are often encountered in engineering practices. To solve this issue, a new prediction approach based on SVR, namely as high-low level (HL-SVR) is proposed for data modeling paper. The consists low-level models larger and high-level model smaller sizes. For each training point one built its corresponding their responses interest. obtained from A number numerical examples validate performance HL-SVR. experimental results indicate that HL-SVR can produce more accurate than SVR. applied stress analysis dental implant, which structural massive samples material implant only be selected Ti alloys. much better design, optimization, systems with
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ژورنال
عنوان ژورنال: International Journal of Computational Methods
سال: 2021
ISSN: ['1793-6969', '0219-8762']
DOI: https://doi.org/10.1142/s0219876221500298