Fast k-NN graph construction for slow metrics
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Updated
Apr 4, 2022 - Python
Fast k-NN graph construction for slow metrics
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The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems
recognize mouse-written numbers using KNN, Neural Network, and Convolutional Neural Network models
This project creates an image of various points, with each pixel colored according to that pixel's distance to the nearest couple of points and their respective colors.
This repository contains the code for a K-Nearest Neighbors (KNN) model built to classify customer segments in Türkiye using the teleCust1000T dataset. The project includes data cleaning, visualization, feature scaling, model training, and evaluation with accuracy metrics.
Implementation of K-Nearest Neighbours (KNN) from scratch
Konu: Kendi Belirlediğimiz bir problem için uzman sistem oluşturma
A KNN algorithm based on the HVDM distance metric powered by decision trees using Weka libraries as a complement developed in Python.
This project uses the K-Nearest Neighbors algorithm to classify individuals into gender categories based on their physical attributes. It assesses the model's performance across different configurations and feature sets.
In this notebook we'll see how to use KNN to classify the IRIS Flowers.
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