Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

The loss function of the traditional support vector machine (SVM) method consists hinge and regularization, which is difficult to achieve quality control observation data. It requires a new measure observed At this stage, researchers will use data cleaning or preprocessing process observational normalize features, make same interval distribution. However, also guarantee accuracy This study uses SVM integrity, unity, proposes function, can obtain uncertainty distribution methods that cannot be obtained. results show algorithm with has better in processing For maximum prediction error data, only 2.58%, reduced by 0.21% compared old function. 2.78%, obvious accuracy.