Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

@inproceedings{Carrington2018KernelMA,
  title={Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care},
  author={Andr{\'e} M. Carrington},
  year={2018}
}
Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual… CONTINUE READING

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