Logistic Regression Classification with Breast Cancer Dataset
Implementing logistic regression classification on the breast cancer dataset achieves 98% accuracy, demonstrating robust predictive performance for benign/malignant tumor classification.
LeNet-5 Training on MNIST Dataset with Enhanced Implementation
This resource implements the LeNet-5 architecture for the MNIST dataset, adapting the original network structure by modifying input dimensions to 28×28 pixels. The implementation draws inspiration from UFLDL tutorials and R. B. Palm's CNN codebase. Key modifications include full connectivity between C3 and S4 feature maps, achieving 99.1% accuracy through optimized training procedures with data augmentation and regularization techniques.
MATLAB Implementation of Naive Bayes Classifier with Code Description
MATLAB code implementation with dataset specifications - This implementation follows the Naive Bayes principle for text classification using numerical encoding for categorical data, featuring training-test split configuration and probability calculation methodology.
Implementation of 10-Fold Cross-Validation
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.
Building Linear Discriminant Functions Using Minimum Squared Error Criterion (MSE Criterion) Through Training Sample Sets
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.
MATLAB Implementation of Support Vector Machine with Iris Dataset Classification
This custom MATLAB implementation of Support Vector Machine (SVM) demonstrates effective pattern recognition when tested on the iris dataset, achieving excellent classification performance through optimized feature analysis and prediction mechanisms.
MATLAB Implementation of DBSCAN Algorithm with Density-Based Clustering
DBSCAN algorithm distinguishes clusters by leveraging density variations in datasets, implemented in MATLAB with epsilon-neighborhood and core point identification.
K-Nearest Neighbors (KNN) Algorithm Implementation with MATLAB Code
MATLAB implementation of K-Nearest Neighbors (KNN) algorithm with accompanying dataset and detailed code explanations
Linear Discriminant Analysis (LDA) for Feature Selection
Linear Discriminant Analysis (LDA) for feature selection enables extraction of discriminative features from datasets or images, commonly applied in machine learning tasks such as classification or clustering. The method involves maximizing class separability through dimensionality reduction.
DBSCAN Clustering Implementation
This file contains a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering code that helps you perform density-based data clustering. The implementation requires three input parameters: your dataset (feature matrix), the minimum number of points required to form a dense region (minPts), and the neighborhood search radius (epsilon). The algorithm automatically identifies core points, border points, and noise points while handling clusters of arbitrary shapes.