Accession Number:



Data Efficient Active Machine Learning

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

University of Wisconsin-Madison

Personal Author(s):

Report Date:


Pagination or Media Count:



Data-efficient machine learning DEML is critical to AFDoD operations for the following reasons. First, training machine learning algorithms generally requires a large and completely labeled training dataset. Human labeling of raw data is an expensive and time-consuming process, especially with a limited pool of expert analysts. Therefore, machine learning algorithms must produce accurate predictive models from limited labeled training data. Moreover, mission environments and objectives can be varied and rapidly changing, and so machine learning models must be quickly adaptable to the situation at hand. The quality of the raw data available to a machine learning system and to human analysts is also often unpredictable. It may often happen that not all of the desired features for making predictions and decisions are available. Therefore, machine learning algorithms must be robust to missing or partially unobserved data.


Subject Categories:

  • Cybernetics
  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

[A, Approved For Public Release]