Explore MATLAB source code curated for "分类器" with clean implementations, documentation, and examples.
A hybrid classifier combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, implementing ensemble learning techniques for enhanced classification performance
MATLAB implementation of a Radial Basis Function (RBF) neural network classifier featuring a 4-3-2 layer configuration, complete with training and testing datasets for performance evaluation
Handwritten character recognition falls within the domain of optical character recognition, employing probabilistic neural networks as classifiers to categorize handwritten digits represented as binary images. The resulting classifier achieves 100% accuracy on training samples, with implementation involving feature extraction and pattern layer optimization.
A comprehensive MATLAB implementation of Logistic Regression (LR) classifier for machine learning, featuring detailed source code and four experimental datasets for practical validation
Deep-net is a deep learning architecture composed of Self-Encoder Algorithm (SEA) for feature extraction and a softmax classifier for categorization. SEA automatically captures the most significant features from input data, and when combined with softmax, enables accurate image classification. This model demonstrates superior performance compared to other approaches in image classification tasks, with implementation typically involving stacked autoencoders for unsupervised pre-training followed by supervised fine-tuning of the classification layer.
Adaboost source code - Adaboost is an iterative algorithm that trains multiple classifiers (weak learners) on the same training dataset and combines them to form a more powerful final classifier (strong learner). The algorithm implementation typically involves weighted training instances and sequential classifier training with error-based weight adjustments.
Feature selection is performed using the t-test method, followed by classification with the Recursive Feature Elimination-Support Vector Machine (RFE-SVM) algorithm
MATLAB source code for Least Mean Squares algorithm implementation, featuring a classifier for pattern recognition applications with adaptive filter implementation
This project implements a straightforward pattern recognition task using Bayes classifier. The training dataset consists of female.txt and male.txt files, while the classifier performance is evaluated using test datasets set1.txt, set2.txt, and set3.txt. The implementation involves experiments with both single features and feature pairs to analyze classification accuracy.
A MATLAB implementation of the Adaboost_M1 algorithm designed for bundling multiple classifiers, representing a fundamental Boosting approach in machine learning