IEEE

Improving KNN Algorithm Efficiency Based on PCA and KD-tree

Abstract: K-nearest neighbor (KNN) algorithm is a simple and widely used classification method in machine learning. This algorithm tries to search every object in the dataset to find the nearest ...
来自MSN

Build k-nearest neighbors from scratch in Python – step by step tutorial

Learn how to implement the K-Nearest Neighbors (KNN) algorithm from scratch in Python! This tutorial covers the theory, coding process, and practical examples to help you understand how KNN works ...
GitHub

knn-python

This project aims to build a complete pattern recognition system to solve classification problems using the k-Nearest Neighbors (KNN) algorithm. To classify chest X-ray images into three categories: ...
IEEE

Minimum-Norm Adversarial Examples on KNN and KNN based Models

Abstract: We study the robustness against adversarial examples of kNN classifiers and classifiers that combine kNN with neural networks. The main difficulty lies in the fact that finding an optimal ...
GitHub

knn-python

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, ...
搜狐

利用sklearn做文本分类(特征提取、knn/svm聚类)

从下载数据集，解压到scikit_learn_data文件夹下，加载数据，详见code注释。 因为我们只取了10000个词，即10000维feature，稀疏度还不算低。而实际上用TfidfVectorizer统计可得到上万维的feature，我统计的全部样本是13w多维，就是一个相当稀疏的矩阵了。 上面代码注释说TF ...