KNN HOME IMPROVEMENT COMPANY LLC
Nearest neighbor interpolation · or k -means clustering . In statistics · [ · 1 ] · and later expanded by Thomas Cover . [ · 2 ] · In classification · a new example is assigned a label based on the labels of its k nearest training examples · in regression · the prediction is computed from the values of those neighbors. [ · Most often · it is used for classification · as a k -NN classifier · typically small). If k   · =  · also known as nearest neighbor smoothing · then the output is simply assigned to the value of that single nearest neighbor · also known as nearest neighbor interpolation . For both classification and regression · a useful technique can be to assign weights to the contributions of the neighbors · so that nearer neighbors contribute more to the average than distant ones. For example
BRISTOL, CT 060109443
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1 Statistical setting 2 Algorithm 3 Parameter selection 4 The 1-nearest neighbor classifier 5 The weighted nearest neighbour classifier 6 Furthest-neighbor variants 7 Properties 8 Error rates 9 Metric learning 10 Feature extraction 11 Dimension reduction 12 Decision boundary 13 Data reduction Toggle Data reduction subsection 13.1 Selection of class-outliers 13.2 Condensed Nearest Neighbor for data reduction 14 k -NN regression 15 k -NN outlier 16 Validation of results 17 See also 18 References 1
BRISTOL, CT 060109443
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