Explore MATLAB source code curated for "作业" with clean implementations, documentation, and examples.
This assignment provides a comprehensive guide for those starting with Support Vector Machines, covering fundamental concepts, implementation approaches, and practical examples using scikit-learn.
Implementation of K-Nearest Neighbors classifier and predictor for data mining course assignments, featuring code-based explanations of distance metrics and voting mechanisms
A comprehensive MATLAB-based filter design assignment submission featuring practical code implementations for various digital filter types and their real-world applications.
A machine learning course assignment implementing PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) for dimensionality reduction. Unlike many online resources with sparse comments, this implementation includes comprehensive annotations and attention to implementation details. Features a comparative Naive Bayes classifier and uses the OLR face image dataset. Important: ReducedDim parameter specifies the exact number of features to extract, not a percentage.
Source code for a fuzzy PID control program, originally developed as an academic assignment, includes comprehensive implementation commentary and algorithm analysis
A comprehensive pattern recognition assignment covering linear classifiers, minimum risk Bayesian classifiers, supervised learning hierarchical clustering analysis, K-L transform for feature extraction, and support vector machines with implementation approaches
Implementation of the ISODATA Algorithm as a Pattern Recognition Course Assignment with Code Integration
Implementation of matched filter for random signal processing (custom-developed coursework solution with practical applications)
Implementation of fractal box-counting dimension algorithm for image analysis - a graduate-level programming assignment with code structure and mathematical foundation
Particle Swarm Optimization applied to the Knapsack Problem. Includes MATLAB source code implementation with detailed algorithm explanations, along with a comprehensive report analyzing the methodology, results, and algorithm performance. This represents a complete academic assignment demonstrating practical optimization techniques.