According to the COMPUTERWORLD article “Artificial Neural Networks,” AND the lecture, which of the following is a method used to train a neural network?
Group of answer choices
Backpropagation
Decision Branching
Chunking
Over-regularization
Backpropagation
According to the information provided, the method used to train a neural network is backpropagation.
To determine the method used to train a neural network, we can refer to the COMPUTERWORLD article "Artificial Neural Networks" and the lecture. We will analyze each of the answer choices to find the correct method.
1. Backpropagation: Backpropagation is a widely-used method for training artificial neural networks. It involves calculating the errors of the network's output and then adjusting the weights of the connections between neurons to minimize those errors. Backpropagation is a popular method due to its effectiveness in training neural networks.
2. Decision Branching: Decision branching, also known as decision trees, is not typically used as a method to train neural networks. Instead, decision trees are a separate machine learning technique that involves creating a tree-like model of decisions and their possible consequences.
3. Chunking: Chunking is a cognitive psychology term used to describe the process of organizing information into meaningful chunks or segments. While chunking can be helpful in understanding and learning neural networks, it is not a specific method used to train them.
4. Over-regularization: Over-regularization is not a method used to train neural networks. It refers to a phenomenon where a neural network becomes excessively tuned to the training data, resulting in poor performance on new or unseen data.
Based on the analysis, the correct method used to train a neural network is backpropagation.