10.1007@978-3-319-33383-0 2 by Unknown

Author:Unknown
Language: eng
Format: epub
Published: 2016-05-20T13:15:21+00:00

d

1 / p

x − x j p =

| (xi) − (xi)j| p

(6.1)

i =1

with parameter p ∈ N. The distance measure corresponds to the Euclidean distance

for p = 2 and the Manhattan distance for p = 1. In other data spaces, adequate

distance functions have to be chosen, e.g., the Hamming distance in B d. For regres-

sion tasks, kNN can also be applied. As continuous variant, the task is to learn a

function ˆ

f : R d → R known as regression function. For an unknown pattern x,

kNN regression computes the mean of the function values of its k-nearest neighbors

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