The 2D feature vectors in the figure below belong to two different classes (circles and rectangles). Classify the object at (6, 6) – in the image represented using a triangle – using k nearest neighbor classification. Use Manhattan distance (L1 norm) as distance function, and use the non-weighted class counts in the k-nearest- neighbor set, i.e. the object is assigned to the majority class within the k nearest neighbors. Perform kNN classification for the following values of k and compare the results with your own “intuitive" result. (a) k = 4 (b) k = 7 (c) k = 10 9 8 7 6. 4 3 2 1 1 2 3 4 5 6 7 8 9
The 2D feature vectors in the figure below belong to two different classes (circles and rectangles). Classify the object at (6, 6) – in the image represented using a triangle – using k nearest neighbor classification. Use Manhattan distance (L1 norm) as distance function, and use the non-weighted class counts in the k-nearest- neighbor set, i.e. the object is assigned to the majority class within the k nearest neighbors. Perform kNN classification for the following values of k and compare the results with your own “intuitive" result. (a) k = 4 (b) k = 7 (c) k = 10 9 8 7 6. 4 3 2 1 1 2 3 4 5 6 7 8 9
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Transcribed Image Text:The 2D feature vectors in the figure below belong to two different classes (circles and rectangles). Classify
the object at (6, 6) – in the image represented using a triangle – using k nearest neighbor classification. Use
Manhattan distance (L1 norm) as distance function, and use the non-weighted class counts in the k-nearest-
neighbor set, i.e. the object is assigned to the majority class within the k nearest neighbors. Perform kNN
classification for the following values of k and compare the results with your own “intuitive" result.
(a) k= 4
(b) k = 7
(c) k = 10
6
4
3
1 2
4 5 6 7 8 9
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