Learning through deterministic assignment of hidden parameters

@article{Fang2018LearningTD,
  title={Learning through deterministic assignment of hidden parameters},
  author={Jian Fang and Shaobo Lin and Zongben Xu},
  journal={IEEE transactions on cybernetics},
  year={2018}
}
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the nonlinear mechanism of an estimator, while the bright parameters characterize the linear mechanism. In a traditional learning paradigm, hidden and bright parameters are not distinguished and trained simultaneously in one learning process. Such a one-stage learning (OSL) brings a benefit of theoretical… CONTINUE READING
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