Form 5495 Minimizing 2017-2023

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Hello so to continue with our pattern recognition the first two lectures we have gone through a general overview of pattern classification we looked at what is the problem of pattern classification we defined what pattern classifiers are now we had looked at the two block diagram model that is given a pattern you first measure some features so pattern gets converted to a feature vector and then the classifier essentially maps feature records to class labels so as I said the code sees about classifier design we looked at a number of classifier design options as an overview so now from this lecture we'll go into details so just to recap what we have done so far we've gone through a general overview of pattern classification a few points from the general overview that I would like to emphasize here is a classifier that we already looked at is the base classifier the base the for the Bayes classifier we're taking a statical view of pattern technician essentially what it means is that a feature vector is essentially random so the variations in feature values when you measure patterns from the same class are captured through probability densities and given all the underlying class conditional densities we seen that Bayes classifier minimizes risk we saw the proof for the only minimizing problem is classification we will see the general proof of this class so Bayes classifier essentially puts a pattern in the class for which the posterior probability is maximum and it minimizes risk if you have the complete knowledge of the underlying probability distributions then Bayes classifier is optimal for minimizing risk there are other classifiers for example we seen nearest neighbor classifier among the other class workers nearest neighbor classifier will come back to again but one...