Pattern Recognition Assignment Programs

In my coursework assignments, I have independently implemented several pattern recognition algorithms. The implementations include the Perceptron Algorithm, Multi-class Perceptron Algorithm, and K-means Clustering. These algorithms are fundamental to classification and clustering tasks, widely applied in machine learning and artificial intelligence domains. The Perceptron Algorithm is a supervised learning method primarily designed for binary classification problems. My implementation features weight initialization, iterative updates using misclassified samples, and convergence checking with a maximum iteration threshold. The core logic involves updating weights using the formula w = w + η*(y - ŷ)*x where η represents the learning rate. The Multi-class Perceptron Algorithm extends the basic perceptron concept to handle multi-class classification problems. The code implementation utilizes one-vs-all strategy, maintaining separate weight vectors for each class and determining classifications through maximum score evaluation. The algorithm incorporates error-correcting updates across multiple decision boundaries. K-means Clustering serves as an unsupervised learning algorithm for grouping unlabeled data. My implementation includes random centroid initialization, Euclidean distance calculation for assignment, and iterative centroid recalculation until convergence. The algorithm features configurable cluster numbers (K value) and incorporates elbow method suggestions for optimal K determination. These algorithms find practical applications in various fields such as image recognition, speech processing, and natural language processing. The code implementations include visualization components for decision boundaries (perceptron algorithms) and cluster plotting (K-means) to enhance interpretability. I welcome any technical questions, implementation suggestions, or optimization recommendations regarding these algorithms. Please feel free to share your insights - I'm eager to discuss and improve these implementations through knowledge exchange.