Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

The Adaline model, which stands for Adaptive Linear Neuron, is indeed classified as a single-layer neural network. This type of network consists of a single layer of output nodes that work directly with the input features without any hidden layers in between.

The main characteristic of Adaline is that it applies a linear activation function to the weighted sum of its inputs. This simplicity makes it particularly suitable for problems that can be solved linearly. The model's training process utilizes a method called the Least Mean Squares (LMS) algorithm, which adjusts the weights based on the error between the predicted output and the actual target value, emphasizing its single-layer architecture.

In contrast, multi-layer perceptrons include one or more hidden layers, while recurrent neural networks are designed for sequential data, incorporating loops that allow information to persist, and convolutional neural networks are primarily used for image data and include convolutional layers to process spatial hierarchies. Therefore, the classification of Adaline as a single-layer neural network is a defining feature of its model structure and operation.