16 December 1992 Asymptotic improvement of supervised learning by utilizing additional unlabeled samples: normal mixture density case
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Abstract
The effect of additional unlabeled samples in improving the supervised learning process is studied in this paper. Three learning processes, supervised, unsupervised, and combined supervised-unsupervised, are compared by studying the asymptotic behavior of the estimates obtained under each process. Upper and lower bounds on the asymptotic covariance matrices are derived. It is shown that under a normal mixture density assumption for the probability density function of the feature space, the combined supervised-unsupervised learning is always superior to the supervised learning in achieving better estimates. Experimental results are provided to verify the theoretical concepts.
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Behzad M. Shahshahani, Behzad M. Shahshahani, David A. Landgrebe, David A. Landgrebe, } "Asymptotic improvement of supervised learning by utilizing additional unlabeled samples: normal mixture density case", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130825; https://doi.org/10.1117/12.130825
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