Defining Decision Contours
To categorize a new sample external to the training set, apply the previous distance metric to find the transition probabilities between and all samples in, with a normalisation. Secondly apply the last two lines of the 3-line algorithm to get cluster, and conditional category probabilities.
Finally we have
Parameter must be kept under close supervision since, as it is increased from zero, increasing numbers of features, in the category probability space, snap into focus at certain critical thresholds.
Read more about this topic: Information Bottleneck Method
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