Final answer:
The term "connection weights" corresponds to the strength of connections between nodes. These weights are crucial in learning algorithms to optimize network performance, and they are adjusted during training processes in neural networks.
Step-by-step explanation:
The term "connection weights" refers to the strength of connections between nodes in various types of networks, such as neural networks in machine learning.
Connection weights determine how much influence one node has on another, and they play a crucial role in learning algorithms where the goal is to optimize these weights for better performance on given tasks. For example, in a neural network, connection weights are adjusted during the training process to minimize errors.
Weight itself can have different connotations depending on the context. In statistics, a scatterplot can show a positive or negative correlation between two variables, such as weight and height, where the points align closely to a straight line if there is a strong correlation. For example, in a neural network, connection weights are used to determine the contribution of each input node to the output node. A higher weight indicates a stronger connection, meaning the input node has a greater influence on the output node's computation. On the other hand, a lower weight signifies a weaker connection, indicating that the input node has less influence on the output node's computation.