Generalized guard-zone algorithm (GGA) for learning: automatic selection of threshold

Pal (Pathak), A. ; Pal, S. K. (1990) Generalized guard-zone algorithm (GGA) for learning: automatic selection of threshold Pattern Recognition, 23 (3-4). pp. 325-335. ISSN 0031-3203

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This work is a continuation of our earlier work on the Generalized Guard-zones Algorithm (GGA) for self-supervised parameter learning. An attempt is made here for the automatic determination of the guard-zone parameter λn (i.e. the threshold used for discarding doubtful or mislabeled samples) at every instant of learning, for the general m-class N-feature pattern recognition problem. This is done by minimizing the mean squared error (MSE) of the estimate, under a simple probabilistic model which takes into consideration the presence of mislabeled training samples. Under the assumptions of normality, it is found that the estimates for λn, so obtained are distribution-free, that is, they do not depend on the parameters of the distribution. They are functions of N, the iteration number n and certain percentage points of the beta distribution with parameters N and n-N. The effectiveness of the automatic selection of guard-zone dimension is further demonstrated on a bivariate three-class data set to show the improvement in performance of the GGA.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:GGA; Learning; Optimum Dimension/Threshold
ID Code:77648
Deposited On:14 Jan 2012 04:23
Last Modified:14 Jan 2012 04:23

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