Abstract
Parametric learning in an unfamiliar environment, i.e., under unknown degree of supervision, forms the topic of this study. In addition to the learning of the distribution parameters, the learning scheme presented here has the novel capability of learning about the unfamiliar teacher, i.e., estimating the teacher characteristics inherent in the environment. This learning, under an unfamiliar teacher hypothesis, is seen to be computationally feasible and the results of simulation reveal the efficacy of the scheme in improving the learning under the unfamiliar teacher through learning about the teacher.
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Dasarathy, B.V., Lakshminarasimhan, A.L. Sequential learning employing unfamiliar teacher hypothesis (SLEUTH) with concurrent estimation of both the parameters and teacher characteristics. International Journal of Computer and Information Sciences 5, 1–7 (1976). https://doi.org/10.1007/BF00991068
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DOI: https://doi.org/10.1007/BF00991068