The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System

@article{Chang2021TheAF,
  title={The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System},
  author={Der Chen Chang and Ophir Frieder and Chi-Feng Hung and Hao-Ren Yao},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.02446}
}
. The distance metric and its nonlinear variant play a substantial role in machine learning, particularly yoso in building kernel functions. Often, the Euclidean distance with a radial basis function (RBF) is used to construct a RBF kernel for nonlinear classification. However, domain implications periodically constrain the distance metrics. Specifically, within the domain of drug efficacy prediction, distance measures must account for time that varies based on disease duration, short to chronic… 

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