Nearest Neighbour Based Classifiers

Part of the Undergraduate Topics in Computer Science book series (UTICS, volume 0)


One of the simplest decision procedures that can be used for classification is the nearest neighbour (NN) rule. It classifies a sample based on the category of its nearest neighbour. When large samples are involved, it can be shown that this rule has a probability of error which is less than twice the optimum error—hence there is less than twice the probability of error compared to any other decision rule. The nearest neighbour based classifiers use some or all the patterns available in the training set to classify a test pattern. These classifiers essentially involve finding the similarity between the test pattern and every pattern in the training set.


Leaf Node Class Label Test Pattern Neighbour Search Near Neighbour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Universities Press (India) Pvt. Ltd. 2011

Authors and Affiliations

  1. 1.Dept. of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

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