Abstract: Computer implemented systems and methods are disclosed for identifying and categorizing electronic documents through machine learning. In accordance with some embodiments, a seed set of categorized electronic documents may be used to train a document categorizer based on a machine learning algorithm. The trained document categorizer may categorize electronic documents in a large corpus of electronic documents. Performance metrics associated with performance of the trained document categorizer may be tracked, and additional seed sets of categorized electronic documents may be used to improve the performance of document categorizer by retraining the document categorizer on subsequent seed sets. Additional seed sets may and categorizations may be iterated through until a desired document categorization performance is reached.

Abstract: A method and apparatus for evaluating whether one or more threat sources is actively tracking an object, such as an aircraft. A tracking system may analyze information regarding signals received from a source and provide a track indication that the source is actively tracking the object without adjusting the dwell arrangement of the scan strategy of the receiver. A track indication may be provided where a sample count of signal intercepts from the source is greater than a track count during a window. A break track indication representing that the source is not actively tracking the object may be provided when a number of signal intercepts from the source is less than a break track count during a window.