Z-value refers to a statistical measurement that describes the position of a data point with the mean of a group of values, measured in terms of standard deviations. The meaning of the z-value is tied to its role in standardizing data, allowing comparisons across different datasets or distributions. In statistical analysis, the z-value (also known as the z-score) is used to determine how far away a specific data point is from the mean, helping to identify outliers or assess the significance of a result in hypothesis testing.
Zero bias refers to a situation in machine learning, particularly in neural networks, where the bias term in a model is set to zero. Bias in the context of neural networks is an additional parameter added to the weighted sum of inputs to a neuron, which helps the model fit the data better by shifting the activation function. The meaning of zero bias is that no such shift occurs, meaning the output of the neuron is solely dependent on the weighted inputs.
Zero-shot learning (ZSL) refers to a machine learning technique where a model is trained to recognize and classify objects or concepts that it has never encountered before. Unlike traditional machine learning approaches that require labeled examples for every class the model needs to identify, zero-shot learning enables the model to make predictions about unseen classes by leveraging knowledge from related classes or by using auxiliary information such as attributes, descriptions, or semantic relationships. The meaning of zero-shot learning is closely tied to its ability to generalize knowledge to new, unseen tasks or categories without needing additional training data.