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Bach, F.R., Lanckriet, G.R.G. and Jordan, M.I. (2004) Multiple Kernel Learning, Conic Duality, and the SMO Algorithm. Proceedings of the Twenty-First International Conference on Machine Learning, Banff, 4-8 July 2004, 8 p.
https://doi.org/10.1145/1015330.1015424

has been cited by the following article:

• TITLE: A Study on Near-Infrared Non-Invasive Blood Glucose Concentration Regression Prediction Based on PSO-MKL-SVR

  AUTHORS: Xinjia Yang, Linhua Zhou

  KEYWORDS: SVM, MKL, PSO, Non-Invasive Blood Glucose

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.1, January 9, 2024

  ABSTRACT: To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.