Explore MATLAB source code curated for "KNN算法" with clean implementations, documentation, and examples.
A hybrid classifier combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, implementing ensemble learning techniques for enhanced classification performance
Implementation of k-nearest neighbors clustering algorithm in MATLAB with code explanations and practical techniques
Implementation of KNN algorithm for 0-9 handwritten digit classification achieving over 90% accuracy, featuring separate training (7291 samples) and testing (2791 samples) datasets. The MATLAB-based solution includes comprehensive documentation with clear code structure and algorithm explanations.
The K-Nearest Neighbors (KNN) algorithm can be utilized for both classification and regression tasks. By identifying k nearest neighbors of a sample and assigning the average of their attributes to that sample, we can predict the sample's properties. A more sophisticated approach involves assigning different weights to neighbors based on their distance, such as making weights proportional to distance, which improves prediction accuracy through weighted averaging.
This project demonstrates the implementation of the K-Nearest Neighbors (KNN) algorithm using MATLAB, including data preprocessing, distance calculation, and performance evaluation with practical code examples.
KNN k-Nearest Neighbors algorithm implementation supporting both Euclidean and Manhattan distance metrics for classification and regression tasks, with configurable k-value parameter.
Implementation of the KNN algorithm with a clear framework and detailed code descriptions, demonstrating classic machine learning methodology
Source code implementation of KNN (K-Nearest Neighbors) algorithm for text classification tasks, featuring comprehensive explanations of key functions and parameter configurations
Complete MATLAB Implementation of K-Nearest Neighbors (KNN) Algorithm with Detailed Code Explanation
MATLAB code implementation of a complete KNN algorithm with detailed explanations of data preprocessing, distance metrics, neighbor selection, and classification voting mechanisms