MATLAB Code Implementation for Support Vector Machine (SVM)
SVM program code for pattern recognition and classification, applicable to image feature processing with enhanced algorithm implementation details.
Multispectral Target Recognition in Remote Sensing Images
Classification of remote sensing imagery using similarity-based methods for multispectral target recognition, with implementation details on feature extraction and comparison algorithms
BP Neural Network for Classification Tasks
MATLAB implementation of BP neural network for classification problems, customizable through parameter modification for various applications
MATLAB Implementation of K-Means Clustering Algorithm
Implementation of K-Means Clustering Algorithm: Given K number of clusters, the algorithm partitions n objects into K classes, maximizing within-cluster similarity while minimizing between-cluster similarity. The implementation involves iterative centroid updates and distance calculations using MATLAB's vectorized operations for efficient clustering.
LIBSVM for Classification and Regression
LIBSVM is a simple, easy-to-use, and efficient software package developed by Professor Lin Chih-Jen and team at National Taiwan University for SVM-based pattern recognition and regression. It provides both precompiled Windows executables and source code for customization, cross-platform adaptation, and algorithm enhancement. The package simplifies parameter tuning with extensive default configurations that handle most practical scenarios while offering cross-validation capabilities. It supports C-SVM, ν-SVM, ε-SVR, ν-SVR models and multi-class classification using one-vs-one strategy, with optimized implementations for large-scale datasets.
MATLAB Code Implementation for Face Recognition
Face Recognition System: PCA Feature Extraction Followed by Neural Network Classification
Implementing Classification and Function Regression using Support Vector Machines (SVM)
Source code for classification and function regression using Support Vector Machines (SVM), including practical examples that can be easily adapted through straightforward modifications.
K-Nearest Neighbors Pattern Recognition Method
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.
Classification of Iris Dataset using Quantum Neural Networks
A custom MATLAB implementation demonstrating quantum neural network classification on the iris dataset, featuring practical code structure and parameter configuration for machine learning applications
Classification of Sample Data Using RBF Neural Network Algorithm
Implementation of RBF Neural Network Algorithm for Classifying Sample Data and Training Dataset with Program Code