Explore MATLAB source code curated for "分类" with clean implementations, documentation, and examples.
Implementation of classification using a Backpropagation Neural Network with Iris dataset, including training process and classification results. Contains comprehensive code annotations and algorithm explanations.
A robust MATLAB toolbox for Support Vector Machines featuring classification, regression fitting, and multiple auxiliary functions. Includes implementation of core SVM algorithms with parameter optimization support for machine learning research and applications.
Source code for a custom-built naive Bayes classifier implemented in MATLAB, suitable for classification and recognition tasks
Hypersphere Support Vector Machine (HSVM), a one-class SVM algorithm that constructs hyperspheres for classification tasks, particularly effective for anomaly detection scenarios
Core implementation of wavelet neural networks for classification tasks, provided as reference material with detailed code annotations and examples.
Support Vector Machine implementation for classification tasks, including dedicated training and test datasets. The package contains six source programs demonstrating practical SVM application with data partitioning strategies.
Classification using Radial Basis Function (RBF) Neural Networks with implementation insights
Character recognition based on Backpropagation Neural Networks. The BP neural network algorithm transforms input-output sample problems into nonlinear optimization problems and solves weight values through iterative gradient descent operations. This implementation uses BP networks for classification with supplemental linear perceptrons for effective single-character recognition. The algorithm features straightforward implementation, high recognition accuracy, and robust performance in various high-noise environments for printed character recognition. Key implementation aspects include gradient computation, weight updating mechanisms, and activation function configuration.
An improved piecewise linear representation approach incorporating four fundamental algorithms: top-down, bottom-up, sliding window, and hybrid sliding window bottom-up techniques. This widely-used dimensionality reduction and classification algorithm features continuous segment generation, overcoming limitations of discrete linear approximations through optimized implementation strategies.
Source code for implementing classification and function regression with Support Vector Machine (SVM), including practical examples. The provided code serves as a ready-to-use template, enabling users to replicate the implementation by following the demonstrated patterns.