What system uses k-nearest neighbor for classification of data examples?

The k-nearest neighbor (KNN) algorithm is a widely used method in the field of supervised learning for classification tasks. In supervised learning, the model is trained using a labeled dataset, where input features are paired with their corresponding output values or categories.

KNN operates by examining the 'k' closest instances (neighbors) in the training data to a given input example and taking a majority vote to determine the category of that input. This requires knowledge of the labeled training data to make decisions, which aligns perfectly with the principles of supervised learning.

In contrast, unsupervised learning does not utilize labeled data, instead seeking to identify patterns or group data without predefined categories. Reinforcement learning involves agents learning to make decisions based on rewards received from their environment rather than from labeled data. Statistical learning is a broader field that includes both supervised and unsupervised methods but does not specifically refer to KNN, which is explicitly a supervised learning technique.

Therefore, the classification of data examples using KNN clearly exemplifies supervised learning principles, wherein the algorithm depends on labeled data to categorize new instances accurately.