Explore MATLAB source code curated for "交叉验证" with clean implementations, documentation, and examples.
MATLAB cross validation implementation that partitions sample sets into training and test datasets to prevent network overfitting, enhancing generalization capability and prediction accuracy through systematic model evaluation
Leave-One-Out Cross-Validation (LOOCV) technique for model evaluation
This program implements random dataset partitioning for 10-fold cross-validation, using algorithmic approaches to shuffle data indices and create balanced folds for robust model evaluation.
A clean and well-structured MATLAB source code for cross-validation, featuring straightforward implementation, easy customization, and efficient execution
MATLAB implementation of polynomial curve fitting for sinusoidal data, incorporating cross-validation techniques (10-fold cross-validation) for model evaluation and prevention of overfitting
MATLAB code for SVM with cross-validation, featuring simple implementation and practical usage for machine learning applications
Implementation and methodology for cross-validation of libsvm parameters c and g with code integration approaches
Data prediction based on Generalized Regression Neural Network (GRNN), featuring cross-validation GRNN neural network prediction program with comparative analysis between BP and GRNN networks. Both networks are trained using identical datasets, demonstrating implementation approaches for neural network optimization and performance evaluation.
A MATLAB-based BP artificial neural network program utilizing cross-validation for parameter optimization, featuring configurable network architecture and performance evaluation metrics
Implementing 10-fold cross validation to evaluate test accuracy, training accuracy, and validation accuracy of a neural network with algorithmic implementation details.