| Abstract |
Machine learning approaches like deep neural networks have proven to be very successful in many domains. However, they require training on a huge volumes of data. While these approaches work very well in a few selected domains where a large corpus of training data exists, they shift the bottleneck in development of machine learning applications to the data acquisition phase and are difficult to use in domains where training data is hard to acquire. For sensor fusion applications in coalition operations, access to good training data that will be suitable for real-life applications is hard to get. The training data sets available are limited in size. For these domains, we need to explore approaches for machine learning which can work with small amounts of data. In this paper, we will look at the current and emerging approaches which allow us to build machine learning models when access to the training data is limited. The approaches examined include statistical machine learning, transfer learning, synthetic data generation, semi-supervised learning and one-shot learning. |