What does "Normal Samples" mean?
Table of Contents
Normal samples are data points that represent typical or expected behavior in a given context. In fields like machine learning and anomaly detection, these samples are used to train models to recognize what is considered "normal." By having a clear picture of normal behavior, systems can more easily spot when something unusual happens.
Importance of Normal Samples
Normal samples play a crucial role in identifying problems or anomalies. When a system is trained with these samples, it learns the usual patterns and can flag anything that strays from them as a possible issue. This is especially important in industries where safety and efficiency are vital.
How Normal Samples Are Used
These samples are collected from various sources to create a reliable baseline for comparison. When new data comes in, it is compared against the normal samples. If the new data shows significant differences, it may indicate an anomaly that needs further investigation.
Conclusion
In summary, normal samples are essential for recognizing what is typical and what is not. They help improve the accuracy of detection systems by providing a benchmark for identifying unusual events or behaviors.