Explore MATLAB source code curated for "分类器" with clean implementations, documentation, and examples.
Pattern recognition implementation of a classifier designed for samples that follow multivariate normal distribution, featuring statistical parameter estimation and classification algorithms.
In machine learning, Random Forest is a classifier comprising multiple decision trees, where the output class is determined by the majority vote of individual tree predictions. Developed by Leo Breiman and Adele Cutler, this algorithm integrates "Bootstrap aggregating" and "random subspace method" for robust ensemble learning. This translation includes MATLAB-specific implementation insights for decision tree training, feature sampling, and aggregation techniques.
This program provides a logistic regression classification implementation with comprehensive usage instructions, offering a downloadable reference for binary classification tasks.
A MATLAB implementation of Adaboost_M1 algorithm, primarily designed for bundling different classifiers. This implementation demonstrates a practical approach to boosting methodology.
Implementation of nonlinear support Vector Machine classifiers for multi-class classification using MATLAB programming environment.
Detailed Code-Oriented Analysis of SVM-Based Text Classification Methodology
C Language Implementation of K-Nearest Neighbors (KNN) Classification Algorithm with Code Examples and Technical Breakdown
License Plate Recognition Implemented with AdaBoost Algorithm
Implementing Support Vector Machines with Cross-Validation in MATLAB for Robust Model Evaluation
Implementing Face Detection with AdaBoost Algorithm and Code Implementation Details