And Unsupervised Pattern Recognition...: Supervised

: Common methods include Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) , k-Nearest Neighbors (k-NN) , and Decision Trees .

: Highly accurate for known classes but requires significant effort to manually label training data. Unsupervised Pattern Recognition (Clustering) Supervised and Unsupervised Pattern Recognition...

: Used for tasks like spam filtering , medical diagnosis , and fraud detection , where historical data can guide future predictions. : Common methods include Linear Discriminant Analysis (LDA),

In this approach, the system is provided with training data that already has known labels. It learns the relationship between specific input features and their corresponding output categories to predict the labels of new, unseen objects. Support Vector Machines (SVM)