Free BCS AIF Practice Exam with Questions & Answers | Set: 2

https://link.springer.com/referenceworkentry/10.1007/978-0-387-73003-5_293#:~:text=Definition,and%20combine%20them% 20to%20use.

It works by selecting different subsets of the data, or different combinations of the hypothesis, and combining the results of each prediction in order to create a single, more accurate result. This is useful in situations where different hypothesis may be accurate in different parts of the data, or where a single hypothesis may not be accurate in all cases. Ensemble learning is used in a variety of applications, from computer vision to natural language processing.

References: [1] BCS Foundation Certificate In Artificial Intelligence Study Guide, BCS [2] Apmg-international.com, "What is Ensemble Learning?", APMG International, https://apmg-international.com/en/about-apmg/blog/what-is-ensemble-learning/  [3] Exin.com, "Ensemble Learning", EXIN, https://www.exin.com/en-us/learn/ensemble-learning

In Machine Learning, the brain’s axons are referred to as nodes. Nodes are the components of a neural network that are responsible for processing the input data and generating the output. A node is a mathematical function that takes input data, performs a computation on it, and produces an output. Each node is connected to other nodes in the network via edges, which represent the strength of the connection between the respective nodes. The strength of the connection between two nodes is determined by the weights assigned to each edge. The weights are adjusted during the training process to generate the desired results.

For more information, please refer to the BCS Foundation Certificate In Artificial Intelligence Study Guide (https://www.bcs.org/upload/pdf/bcs-foundation-certificate-in-artificial-intelligence-study-guide.pdf)  or the EXIN Artificial Intelligence Foundation Certification (https://www.exin.com/en/exams/artificial-intelligence-foundation).