Explore MATLAB source code curated for "IRIS数据集" with clean implementations, documentation, and examples.
While MATLAB provides built-in Naive Bayes functions, programming it from scratch deepens understanding. This example demonstrates a custom implementation of Naive Bayes classification using the IRIS dataset, including data preprocessing, probability estimation, and performance evaluation.
Pattern Recognition Assignment - Fully Custom Simulation Program. The implementation first applies Principal Component Analysis (PCA) for dimensionality reduction on the IRIS dataset, then classifies the reduced-dimensional data using the minimum error method. The compressed package includes MATLAB source code with detailed comments, a self-written report, and the IRIS dataset in .MAT format for program invocation. The program outputs final results to a text file with clear algorithmic implementation explanations.
This program implements multiclass classification on the IRIS dataset, featuring detailed training procedures and prediction accuracy evaluation. The implementation includes comprehensive classification modeling and achieves high precision, successfully demonstrating multiclass classification experiments.
A practical demonstration of RBF neural network implementation using the IRIS dataset for multi-class classification
ISODATA Clustering Algorithm featuring detailed code annotations and implementation using the Iris dataset. Includes explanations of key algorithmic steps such as centroid calculation, cluster merging/splitting mechanisms, and distance threshold configurations.
Implementation of Bayes Classifier for IRIS Dataset Classification with Feature Analysis and Performance Enhancement
Cloud Classifier: A Classification Program Based on Cloud Model Theory with Iris Dataset Testing Implementation
Classification experiments on the Iris dataset featuring linear classifier implementation, BP neural network classifier, and XOR data classification using BP networks, accompanied by a comprehensive experimental report with code-level insights
This project implements clustering on the IRIS dataset using the Shuffled Frog Leaping Algorithm (SFLA), featuring both shuffling and leap-frogging operators with comprehensive code implementation demonstrating population evolution and optimization mechanisms.
Comprehensive implementation of various classification algorithms applied to the iris dataset, featuring code-level explanations of LMS, MSE, and HK approaches with performance comparisons.