Explore MATLAB source code curated for "测试集" 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
This paper applies the Minimum Squared Error Criterion (MSE Criterion) to construct linear discriminant functions from training datasets and utilizes these functions for test set classification. The implementation uses three feature datasets: 1) Gender data (male/female), 2) SONA academic metrics, and 3) UPS performance scores, with Python/numpy implementations for matrix operations and weight optimization.
I. Clear environment variables II. Generate training/test datasets III. Create, train, and simulate BP neural network IV. Performance evaluation V. Visualization plotting Detailed demonstration of BP neural network implementation with visualizations and code examples
Champion code shared by top programmers who achieved state-of-the-art performance on object tracking evaluation datasets
Application Background Developed by Sun Chengyi et al. in 1998, the Mind Evolutionary Algorithm (MEA) serves as an effective optimization technique. This chapter details MEA's fundamental concepts and implements the algorithm in MATLAB through a nonlinear function fitting case study. Key Technologies 1. Training/Test Set Generation: Creating datasets using MATLAB's rand() and linspace() functions with proper data partitioning 2. Initial Population Initialization: Implementing population initialization with bounds checking using unifrnd() function 3. Subpopulation Convergence Operation: Performing crossover operations with tournament selection and simulated binary crossover (SBX) 4. Subpopulation Dissimilation Operation: Applying mutation operations using polynomial mutation with adaptive mutation rates 5. Optimal Individual Analysis: Implementing fitness evaluation and elite preservation techniques 6. BP Neural Network Training: Configuring network architecture with newff() and optimizing weights using MEA-based training 7. Simulation Testing and Result Analysis: Conducting performance evaluation with MSE metrics and convergence curve plotting
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.
This article implements Dense SIFT for image feature extraction combined with Bag-of-Words (BoW) modeling. The BoW dictionary is constructed using only the training set since the test set is unavailable during development. The implementation covers BoW concept visualization, SVM classification with RBF kernel, and introduces a custom histogram intersection kernel based on research findings. The workflow includes feature encoding and demonstrates custom kernel integration in SVM.
MATLAB Code Implementation for SVM Classification