KNN - K-Nearest Neighbors Algorithm
K-Nearest Neighbors (KNN) algorithm for classification and regression tasks
Indoor Fingerprint Localization Algorithms: NN, KNN, and WKNN Implementation and Comparison
Implementation and comparative analysis of indoor fingerprint localization algorithms including Nearest Neighbor (NN), K-Nearest Neighbors (KNN), and Weighted K-Nearest Neighbors (WKNN). The code is executable with comprehensive comments, featuring algorithm explanations and key function descriptions.
K-Nearest Neighbors (KNN) for Pattern Recognition
K-Nearest Neighbors (KNN) implementation for pattern recognition, featuring a provided dataset suitable for beginners to deepen understanding of the KNN algorithm and its practical implementation.
K-Nearest Neighbors (KNN) Algorithm Implementation with MATLAB Code
MATLAB implementation of K-Nearest Neighbors (KNN) algorithm with accompanying dataset and detailed code explanations
Classification Algorithms: SVM, kNN, Decision Trees and More
Comprehensive collection of classification algorithms including SVM, kNN, and Decision Trees with detailed documentation and code implementation insights
Research on Indoor Pedestrian Localization and Tracking Algorithms
Investigation of indoor pedestrian localization and tracking algorithms, including: RSS-based KNN indoor localization algorithm, RSS-based Kalman filter algorithm, and Particle filter algorithm integrating RSS and Dead Reckoning (DR) with code implementation considerations.
C Language K-Nearest Neighbors (KNN) Classifier with Implementation Examples
A C language implementation of K-Nearest Neighbors (KNN) classifier featuring 5 practical learning examples with code explanations
AdaBoost + KNN + LBP Face Recognition Code: Classic Algorithms with Implementation Details
Implementation of classic face recognition algorithms using AdaBoost, KNN, and LBP feature extraction with code explanations and technical insights
Face Recognition Implementation Using PCA+KNN Algorithm with 2DPCA Approach
Implementation of face recognition using PCA+KNN algorithm with 2DPCA-based methodology, offering reduced computational time and enhanced efficiency through matrix-based feature extraction.
Support Vector Machine (SVM) for Enhanced k-Nearest Neighbor Classification with Dimensionality Reduction
This paper demonstrates the application of Support Vector Machine (SVM) as a robust foundation for improving k-nearest neighbor (kNN) classifiers. We introduce Discriminant Analysis via Support Vectors (SVDA), a novel multi-class dimensionality reduction technique that leverages SVM principles. The implementation involves using only support vectors to compute transformation matrices, reducing computational overhead for kernel-based feature extraction. Our methodology extends to non-linear versions through kernel mapping, achieving improved recognition performance in experimental validations across standard datasets.