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K-nearest neighbors algorithm

In statistics and machine learning, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method that assigns weightage only to the k (number of) nearest neighbors of an entity in making a decision about the entity. It is used both in classification -- where a new example is assigned a label based on the labels of its k nearest…

Feature extraction, Algorithm & Dimension reduction

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Overview

Algorithm

Parameter selection

The 1-nearest neighbor classifier

The weighted nearest neighbor classifier

Furthest-neighbor variants

Properties

Error rates

Metric learning

Feature extraction

Dimension reduction

Decision boundary

Data reduction

K-NN regression

K-NN outlier

Validation of results

Advanced semantic analysis

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Map overview Semantic statistics

K-nearest neighbors algorithm

Nodes84
Edges83
Triples2
Avg. degree1.98
Density0.02381
Components1

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Important terminology

nearest k-nn data classification neighbor training algorithm neighbors class set distance example also regression displaystyle examples points point classifier classes

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
large margin nearest neighbor or neighborhood components analysis.A drawback of the basicinstance ofthe classification accuracy of k-NN can be improved significantly if the distance metric is learned with specialized algorithms0.80text
likelihood-ratio test can also be appliedinstance ofMore robust statistical methods0.80text

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