Pattern matching
In machine learning, one of the most important tasks is to label or classify data based on some criteria or patterns. However, labeling data manually can be time consuming and costly, especially when dealing with a large amount of data. By leveraging predefined patterns, this labeling approach enables the automatic assignment of meaningful categories or classes to data instances.
Pattern matching involves the identification of specific patterns or sequences within data that can be used as indicators for assigning labels. These patterns can be defined using regular expressions, rule-based systems, or other pattern recognition algorithms. The objective is to capture relevant information and characteristics from the data that can be matched against predefined patterns to infer labels accurately.
Pattern matching can be applied to various domains and scenarios in machine learning. Some common applications include the following:
