White box radial basis function classifiers with component selection for clinical prediction models

@article{Belle2014WhiteBR,
  title={White box radial basis function classifiers with component selection for clinical prediction models},
  author={Vanya Van Belle and Paulo J. G. Lisboa},
  journal={Artificial intelligence in medicine},
  year={2014},
  volume={60 1},
  pages={53-64}
}
OBJECTIVE To propose a new flexible and sparse classifier that results in interpretable decision support systems. METHODS Support vector machines (SVMs) for classification are very powerful methods to obtain classifiers for complex problems. Although the performance of these methods is consistently high and non-linearities and interactions between variables can be handled efficiently when using non-linear kernels such as the radial basis function (RBF) kernel, their use in domains where… CONTINUE READING
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