Nearest neighbor classification
The closest neighbor classification is a nonparametric method for estimating probability density functions . The resulting k-Nearest-Neighbor-Algorithm ( KNN , in English "k-nearest-neighbor algorithm") is a classification method in which a class is assigned taking its closest neighbors into account . The part of the learning consists of simply saving the training examples, which is also known as lazy learning ("sluggish learning"). Data normalization can increase the accuracy of this algorithm.
k nearest neighbor algorithm
The classification of an object (often described by a feature vector ) takes place in the simplest case by majority decision. The k next already classified objects of participate in the majority decision . Many distance measures are conceivable ( Euclidean distance , Manhattan metric , etc.). is assigned to the class that has the greatest number of objects of these neighbors. For two classes a tie in the majority decision can be prevented by an odd one.
For a small one, there is a risk that noise in the training data will worsen the classification results. A Voronoi diagram results for . If the choice is too large, there is a risk of including points with a large margin in the classification decision. This risk is particularly great if the training data are not evenly distributed or if only a few examples are available. If the training data is not evenly distributed, a weighted distance function can be used, which assigns a higher weight to closer points than to points further away. A practical problem is also the storage and computation effort of the algorithm in the case of high-dimensional spaces and a lot of training data.
- Pattern recognition
- Feature space
- Scikit-learn a free machine learning software library for the Python programming language
- OpenCV a free program library with algorithms for image processing and machine vision
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