Breast Tumor Diagnosis Using LVQ Neural Network Classification in MATLAB
Implementing LVQ Neural Network Classification for Breast Tumor Diagnosis in MATLAB with Code Implementation Details
MATLAB Implementation of Data Mining Algorithms
MATLAB data mining algorithms featuring CART decision tree for multi-class classification. Decision tree algorithm implementation with practical classification capabilities.
Classification of Extracted Feature Vectors Using Fuzzy Support Vector Machines
This project demonstrates dimensionality reduction of ORA face images using Principal Component Analysis (PCA), followed by high-accuracy classification of extracted feature vectors through Fuzzy Support Vector Machines (FSVM).
Fuzzy Independent Component Analysis + Principal Component Analysis for Face Recognition
Fuzzy Independent Component Analysis combined with Principal Component Analysis for face recognition, utilizing Fuzzy Support Vector Machines for classification with implementation of feature extraction and pattern recognition algorithms.
Two-Dimensional Three-Class Sample Classification with Decision Boundary Visualization
This program implements classification for two-dimensional three-class samples with decision boundary plotting functionality. Essential for pattern recognition learners and neural network enthusiasts. Features straightforward MATLAB code implementation using statistical classification methods with strong practical applicability.
Fuzzy Minimum Support Vector Machine
An implementation of fuzzy minimum support vector machine for classification, demonstrating superior performance compared to conventional classification algorithms through enhanced fuzzy membership functions and optimized margin calculation.
Relevant Vector Machine Toolbox Versions 1 and 2
Relevant Vector Machine Toolbox versions 1 and 2 supporting regression and classification tasks with executable demonstration files
SVM GUI Enables Intuitive Interface Operations for Support Vector Machine Implementation
The SVM GUI facilitates user-friendly interface operations for Support Vector Machines, offering superior capabilities compared to neural networks including classification, recognition, regression analysis, and anomaly detection with robust kernel function implementations.
Iris Data Classification Using Naive Bayes Method
Implementation of Iris data classification with Naive Bayes algorithm, including comprehensive experimental report with code analysis and performance evaluation
SVM Classification Code for Wine Category Testing
Support Vector Machine classification implementation for red wine category prediction with feature analysis