Explore MATLAB source code curated for "分类" with clean implementations, documentation, and examples.
MATLAB implementation of the Random Forest algorithm providing reliable classification capabilities with comprehensive functionality, though execution speed may be slower compared to optimized implementations
Backpropagation Neural Network Algorithm for Classification and Recognition with Practical Code Implementation
Implementation of Gaussian Process algorithms for regression and classification tasks, accompanying the book "Gaussian Process-Based Machine Learning." This distribution contains the latest v3.1 version updated on 2010-09-27, featuring enhanced kernel functions and optimization methods for improved predictive performance.
LVQ Neural Network Classification Implementation for Breast Tumor Diagnosis with Code Integration
Implementation of clustering and classification routines using SVM algorithm. Includes experimental datasets, execution results, and a classic reference paper titled "A New Fuzzy Cover Approach to Clustering". Code enhancements demonstrate feature scaling, kernel selection, and hyperparameter optimization techniques.
A MATLAB implementation of classic Support Vector Machine (SVM) for classification tasks, featuring identification, categorization, and parameter optimization capabilities with detailed algorithmic explanations.
MATLAB implementation of XOR problem classification based on Backpropagation Neural Network
A graduation programming project implementing wavelet transform and support vector machines (SVM) for power quality disturbance identification and classification, primarily utilizing energy-based features for signal characterization.
RBF Neural Network implementation for classification and regression tasks, featuring practical code examples and key algorithmic explanations - highly recommended for download
Implementation of logistic regression algorithm in machine learning capable of classifying samples from three Gaussian distributions, featuring mathematical foundations and practical coding approaches