Fisher Classification Algorithm, Perceptron Algorithm, Least Squares Algorithm, Fast Nearest Neighbor Algorithm, K-Nearest Neighbors Method, Edited Nearest Neighbor and Condensed Nearest Neighbor Methods, Binary Decision Tree Algorithm

In this article, we introduce seven commonly used machine learning algorithms. These algorithms include Fisher Classification Algorithm, Perceptron Algorithm, Least Squares Algorithm, Fast Nearest Neighbor Algorithm, K-Nearest Neighbors Method, Edited Nearest Neighbor and Condensed Nearest Neighbor Methods, and Binary Decision Tree Algorithm. The Fisher Classification Algorithm, widely used in pattern recognition and computer vision, effectively separates data into two or more classes by maximizing between-class variance while minimizing within-class variance. The Perceptron Algorithm serves as a binary linear classifier that computes the dot product between input vectors and weight vectors, applying a threshold function (typically using sign() or step functions) to produce classification outputs. The Least Squares Algorithm performs linear regression by minimizing the sum of squared residuals, often implemented through matrix operations like the normal equation: (X^T X)^{-1} X^T y. The Fast Nearest Neighbor Algorithm accelerates classification and regression tasks by efficiently searching for the closest training samples using optimized data structures like KD-trees, then predicting labels based on nearest neighbors' attributes. K-Nearest Neighbors Method extends this concept by considering K closest neighbors (commonly using Euclidean distance metrics) and performing majority voting for classification or averaging for regression. Edited Nearest Neighbor and Condensed Nearest Neighbor Methods enhance neighbor-based algorithms by selectively removing redundant data points (editing) or compressing the training set to retain only critical boundary points, improving computational efficiency without sacrificing accuracy. Finally, the Binary Decision Tree Algorithm constructs tree-structured classifiers through recursive binary splits (using criteria like Gini impurity or information gain), where each node represents a feature threshold decision until reaching leaf nodes containing final predictions.