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Citation bot (talk | contribs) Added bibcode. | Use this bot. Report bugs. | Suggested by Dominic3203 | Category:Machine learning algorithms | #UCB_Category 66/84 |
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| pages = 15–27
| year = 1982
| bibcode = 1982AcAC..136...15C }}
</ref> One way to overcome this problem is to weight the classification, taking into account the distance from the test point to each of its ''k'' nearest neighbors. The class (or value, in regression problems) of each of the ''k'' nearest points is multiplied by a weight proportional to the inverse of the distance from that point to the test point. Another way to overcome skew is by abstraction in data representation. For example, in a [[self-organizing map]] (SOM), each node is a representative (a center) of a cluster of similar points, regardless of their density in the original training data. ''K''-NN can then be applied to the SOM.
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