The Perceptron Algorithm and the Kernel Trick

The Perceptron Algorithm is one of the earliest and most influential machine learning models, forming the foundation for modern neural networks and support vector machines (SVMs). Proposed by Frank Rosenblatt in 1958 (Rosenblatt, 1958), the perceptron is a simple yet powerful linear classifier designed for binary classification tasks.  Despite its simplicity, the perceptron introduced key concepts that remain central to machine learning today, such as iterative weight updates, the use of activation functions, and learning a decision boundary (Goodfellow, Bengio & Courville, 2016). These ideas have directly influenced the development of multi-layer neural networks by introducing weight adjustment rules that underpin backpropagation (LeCun, Bengio & Hinton, 2015).