Application of Gaussian Processes in Regression and Classification Problems
Implementation of Gaussian Processes with Detailed Annotations for Regression and Classification Problems, Including Code Examples
Support Vector Machine Classification - Breast Cancer Dataset
This code provides comprehensive implementation details with thorough testing, ready for direct deployment in classification tasks using SVM algorithms.
Imbalanced Dataset Classification using Extreme Learning Machine
Extreme Learning Machine (ELM) based classification for imbalanced datasets, delivering exceptional performance with fast processing speeds
Unsupervised Learning Neural Network Classification - Water Source Identification
The code is thoroughly documented and tested, ready for immediate implementation. The neural network utilizes unsupervised learning algorithms for automated pattern recognition in water source classification tasks.
Classification of Image Features Using Random Forest Algorithm
Simulation Implementation of Random Forest Algorithm for Image Feature Classification
Implementing Naive Bayes Classification with the IRIS Dataset
While MATLAB provides built-in Naive Bayes functions, programming it from scratch deepens understanding. This example demonstrates a custom implementation of Naive Bayes classification using the IRIS dataset, including data preprocessing, probability estimation, and performance evaluation.
Bayesian Network MATLAB Implementation
MATLAB source code for Bayesian networks with classification capabilities, featuring probabilistic modeling, inference algorithms, and network structure learning
Binary Data Classification Using Naive Bayes Classifier with Implementation Guide
Implementing Naive Bayes Classifier for binary data classification with detailed code explanations and visualization of accuracy metrics through comprehensive plotting
Random Forest Algorithm: An Excellent Machine Learning Approach
The Random Forest algorithm serves as a highly effective method for feature classification in machine learning applications, utilizing ensemble learning techniques for robust performance.
Classical Algorithm for Pattern Recognition: Dynamic Clustering with K-means
Implementation of k-means algorithm for dynamic clustering using MATLAB with graphical analysis of classification results, exploring algorithm performance and optimization approaches.