MATLAB Implementation of Support Vector Machine Method
Support Vector Machine method implemented in MATLAB for classification detection, pattern recognition, and face detection applications with code-based implementation details
2D Dataset Parzen Window Non-Parametric PDF Estimation with 3D Visualization
Implementation of Parzen window non-parametric probability density function estimation for 2D datasets, featuring 3D visualization results. Includes complete documentation, program execution instructions, MATLAB source code, and graphical outputs. Developed as a pattern recognition assignment focusing on kernel density estimation techniques.
Pattern Recognition Toolbox Functions
Pattern Recognition Toolbox Functions featuring multiple recognition methods including KNN, PCA, SVM, C4.5, EM algorithm with code implementation details and technical specifications
Pattern Recognition: Fuzzy Clustering Algorithms, Transitive Closure Method, and Tracking Method
Pattern Recognition - Fuzzy Clustering Algorithms: Implementation of Transitive Closure Method and Tracking Method in MATLAB
K-Nearest Neighbors (KNN) for Pattern Recognition
K-Nearest Neighbors (KNN) implementation for pattern recognition, featuring a provided dataset suitable for beginners to deepen understanding of the KNN algorithm and its practical implementation.
MATLAB Machine Learning Code Implementation
Comprehensive MATLAB source code for machine learning, featuring multi-class SVM algorithms, pattern recognition systems, feature selection methods, and various regression techniques with practical implementation examples.
Learning and Training of Deep Boltzmann Machine (DBM) - A Novel Neural Network Approach with MATLAB Implementation
MATLAB code implementation for learning and training of Deep Boltzmann Machines (DBM), a novel neural network architecture with significant applications in pattern recognition and image processing domains. This resource provides practical coding examples and algorithm explanations suitable for researchers and learners to study and adapt.
ISODATA Clustering Experiment for Pattern Recognition Course
Source code implementation for the ISODATA clustering experiment in pattern recognition curriculum
Neural Network-Based Character Recognition
This project implements character recognition using neural networks, a classic pattern recognition application. The source code trains a Backpropagation (BP) neural network to recognize 26 English letters, demonstrating key implementation details including network architecture, training methodology, and feature extraction techniques.
Wavelet Packet Decomposition and BP Neural Network Training with Pattern Recognition Applications
Comprehensive implementation of wavelet packet decomposition and backpropagation neural network training algorithms for pattern recognition and signal analysis, featuring multi-scale feature extraction and deep learning capabilities.