2 results about "Support vector regression machine" patented technology

This invention relates to the field of vancomycin pharmacokinetic technology, specifically to the design of a vancomycin clearance prediction scheme based on a VPB ensemble model. This method, based on an ensemble learning strategy, constructs a blending ensemble model to predict vancomycin clearance in adult Chinese patients. First, a variational autoencoder is used to amplify the original sample data to increase data diversity. Then, a particle swarm optimization algorithm is introduced to optimize the parameters of multiple base learners, and the prediction results of the optimized base learners are used as new feature inputs. Finally, a support vector regression machine is used as a meta-learner to integrate and model the above features, forming the final vancomycin clearance prediction model. The VPB model constructed in this invention achieves a determination coefficient R² exceeding 0.9 on both the test and training sets, demonstrating superior prediction accuracy compared to population pharmacokinetic models.

This invention provides a method, apparatus, equipment, and medium for predicting shale gas content. The shale gas content prediction method includes the following steps: confirming the geological parameters of the target shale and performing grey relational analysis on the geological parameters and shale gas content to identify the main controlling factors of shale gas content; establishing a support vector regression (SVR) model, optimizing the parameters of the SVR model, outputting the optimal parameters, and constructing an optimal SVR model; training the optimal SVR model based on the main controlling factors of shale gas content to obtain the shale gas content prediction model; and predicting the gas content of the target shale based on the shale gas content prediction model. This invention uses grey relational analysis to select the main controlling factors and optimize the support vector regression (SVR) model, thus solving the problem that the performance of the SVR model depends on the selection of its hyperparameters.