Explore MATLAB source code curated for "训练集" 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
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
Implementing image recognition with the k-nearest neighbors method, featuring a straightforward program structure that requires a training dataset for model development and classification tasks.
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.
Cross-validation methods for image classification, which enable the acquisition of more accurate parameter values from training datasets. These algorithms involve partitioning data into subsets and systematically validating model performance through iterative training cycles.
Source code implementation of the world's most advanced speaker recognition algorithm, including complete training and testing datasets for model validation and performance evaluation.
Linde, Buzo, and Gray (LBG) introduced a vector quantization (VQ) design algorithm utilizing training sequences, which eliminates multidimensional integration requirements. The iterative LBG algorithm processes extensive vector sets per iteration, with key implementations involving codebook initialization, nearest-neighbor classification, and centroid updates for progressive optimization.
Utilizing MATLAB's built-in toolbox functions, this project implements trend prediction for temperature, precipitation, and atmospheric pressure across 12 months in Beijing during 2009, achieving favorable results. Key components include: BP neural network implementation (Bp.m), MATLAB program for BP neural network (bp_ds.xls), training set input (bp_nds.xls), training set output/target (bp_td.xls), test set input (bp_ntd.xls), test set output (BP_weather_prediction.doc), and related thesis documentation. The implementation leverages backpropagation algorithm for time-series forecasting with optimized hyperparameter configuration.