Lecture 19 Blog, ECE662 Spring 2012, Prof. Boutin
Tuesday March 20, 2012 (Week 10)
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Coming back from Spring break, today we covered the k-nearest neighbor (KNN) density estimation technique, along with the k-nearest neighbor (KNN) pattern recognition method. More specifically, we presented a formula for estimating a density function at a point based on some samples drawn from that density, and we showed that it is an unbiased estimate of the true value of the density at that point. We also showed how this formula is the basis for using the "majority vote among neighbors" rule for assigning a category to data.
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