Linear SVM Algorithm for Classifier Design
Linear SVM algorithm for designing a classifier to perform classification on datasets with implementation insights
Generating Base SVM Classifiers Using AdaBoost Method
Creating base Support Vector Machine classifiers through AdaBoost algorithm and integrating recognition results using simple voting method. Includes descriptions of SVM toolbox and AdaBoost algorithm workflow with code implementation insights.
Various Classifiers for IRIS Dataset Classification
Multiple classifiers implementing unsupervised classification of multi-dimensional sampling points on the IRIS dataset. Classifiers can be adapted based on the number of categories, featuring partial code from pattern recognition assignments with implementation insights for machine learning workflows.
Nearest Neighbor and K-Neighbor Classifier
A MATLAB implementation of Nearest Neighbor and K-Nearest Neighbors classifiers with clear code structure, ideal for beginners learning machine learning algorithms
SVM Multi-Class Classification Using One-vs-One Strategy
This method selects samples from one class as positive examples while using samples from only one other class as negative examples for each binary classifier (known as "One-vs-One" approach). This prevents dataset skewness. The process involves creating multiple classifiers - the first determines "Class 1 vs Class 2", the second "Class 1 vs Class 3", and so on. For k classes, the total number of binary classifiers required is k(k-1)/2 (e.g., 10 classifiers for 4 classes). Although more classifiers are needed, the total training time is significantly reduced compared to the "One-vs-Rest" method.
Cloud Model-Based Classifiers
In the MATLAB environment, cloud model-based classifiers including example swarm-optimized cloud classifier and attribute similarity cloud classifier implementations.
Random Forest Classifier
Random Forest is an ensemble classifier comprising multiple decision trees, where the final output class is determined by the majority vote of individual tree predictions. The implementation includes practical examples that can be executed to demonstrate the algorithm's functionality. Key advantages include high accuracy across diverse datasets, robust handling of numerous input variables, built-in feature importance evaluation, and unbiased generalization error estimation during training.
Classifier with Graphical User Interface
MATLAB software package featuring classifier algorithms with GUI support, primarily designed for binary classification while most programs are extensible to multi-class scenarios
Pattern Recognition Classifiers: Fisher and Bayes Classifiers with MATLAB Implementation
MATLAB implementation of pattern recognition classifiers including Fisher and Bayes classifiers for gender classification tasks, featuring statistical analysis and probability-based decision making
An SVM Classifier for Pedestrian Detection
This is an SVM classifier designed for classifying training samples in pedestrian detection applications, featuring direct MATLAB implementation with built-in support for key machine learning functions.