I'm doing an exercise about k-NN, k-Neighbor classifier. And I don't understand the following sentence:
Show that for all x ∈ $R^d$ which have a unique nearest neighbor amongst the points in {x1, . . . , xn} there exists an $h_0 > 0$ such that for all $h < h_0$ the resulting SVM prediction is the same as the prediction made by a Nearest Neighbor (1-NN) classifier.
What is meant by unique nearest neighbor? I know what k-Neighbour classifier is, but what is the nearest neighbor?
Happy Holidays
Sometimes it is possible to have neighbors that are equidistance.
The question is describing points of which there is exactly one nearest neighbors, those points do not have two neighbors that share the minimum distance from it.