Explore MATLAB source code curated for "模式识别" with clean implementations, documentation, and examples.
Feature extraction of radar signals to achieve pattern recognition, including source code implementation with algorithms like FFT, wavelet transforms, and statistical feature calculation methods
MATLAB implementation of the K-means algorithm, an essential fuzzy clustering method in pattern recognition, featuring code structure explanation and clustering mechanics.
A refined MATLAB implementation of the Active Shape Model (ASM) algorithm originally developed by Cootes. This enhanced version builds upon Cootes' classroom source code with critical bug fixes and adds convenient sample training label storage functionality. Designed for image analysis, pattern recognition, and artificial intelligence researchers, the implementation includes improved data handling and model training workflows.
Bayesian image classification combined with neural networks for pattern recognition, implementing probability-based decision making and deep learning models
A collection of highly useful source code implementations primarily focused on machine vision and pattern recognition applications, featuring practical algorithm implementations and key function descriptions
A graduate-level assignment on nonlinear classifiers in pattern recognition from Tsinghua University, featuring comprehensive problem analysis with practical code implementation insights.
The IRIS dataset is primarily employed in clustering methods for pattern recognition and image segmentation tasks, with implementations often involving algorithms like K-Means or hierarchical clustering.
Using mushrooms dataset to simulate pattern classification methods [Linear Classification, Bayesian Classification, Parzen Window, KNN] along with feature selection and dimensionality reduction techniques [PCA, LDA], including implementation code descriptions and comparative results analysis for each classification approach.
This approach employs Discrete Wavelet Transform (DWT) for feature extraction and Principal Component Analysis (PCA) for dimensionality reduction, applicable to pattern recognition systems including facial recognition, palmprint analysis, emotion detection, and fingerprint identification.
Distance-based feature selection with genetic algorithm optimization for AI and pattern recognition applications, implementing dimensionality reduction through fitness evaluation and chromosome operations.