The primary difference between and unsupervised pattern recognition lies in whether the data used for training is "labeled" or "unlabeled". Supervised recognition uses a teacher-like approach with predefined categories, while unsupervised recognition acts like a discoverer, finding inherent structures on its own. Supervised Pattern Recognition (Classification)
: Common methods include Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) , k-Nearest Neighbors (k-NN) , and Decision Trees . Supervised and Unsupervised Pattern Recognition...
: Highly accurate for known classes but requires significant effort to manually label training data. Unsupervised Pattern Recognition (Clustering) Support Vector Machines (SVM)