Distance-Weighted Nearest Neighbor Algorithm
- The refinement to the k-NEAREST NEIGHBOR Algorithm is to weight the contribution of each of the k neighbors according to their distance to the query point xq, giving greater weight to closer neighbors.
- For example, in the k-Nearest Neighbor algorithm, which approximates discrete-valued target functions, we might weight the vote of each neighbor according to the inverse square of its distance from xq
Distance-Weighted Nearest Neighbor Algorithm for approximation a Real-valued target functions
Terminology
- Regression means approximating a real-valued target function.
- Residual is the error 𝑓̂(x) - f (x) in approximating the target function.
- Kernel function is the function of distance that is used to determine the weight of each training example. In other words, the kernel function is the function K such that
wi = K(d(xi, xq))
Labels: Machine Learning, Machine Learning Unit-5
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