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This repository has been archived by the owner on Nov 19, 2020. It is now read-only.
This task involves the creation of a new sample application to demonstrate how to use k-NN (k nearest neighbors).
The application can be based on any of the other existing applications, such as the Neural Networks Classification sample application. It is possible to copy and paste this application and then replace the Neural Networks part with K-Nearest Neighbors instead.
It should also be possible to compare the performance and execution speed of kNN with and without using KD-Trees. To create a k-NN that uses kd-trees, simple use the standard KNearestNeighbors class. To create a k-NN without using kd-trees, create the generic version using KNearestNeighbors<T> instead.
The text was updated successfully, but these errors were encountered:
This task involves the creation of a new sample application to demonstrate how to use k-NN (k nearest neighbors).
The application can be based on any of the other existing applications, such as the Neural Networks Classification sample application. It is possible to copy and paste this application and then replace the Neural Networks part with K-Nearest Neighbors instead.
It should also be possible to compare the performance and execution speed of kNN with and without using KD-Trees. To create a k-NN that uses kd-trees, simple use the standard KNearestNeighbors class. To create a k-NN without using kd-trees, create the generic version using KNearestNeighbors<T> instead.
The text was updated successfully, but these errors were encountered: