MACHINE LEARNING WITH INCOMPLETE INFORMATION

Machine learning algorithms detects patterns, regularities, and rules from the training data and adjust program actions accordingly. For example, when a learner (a computer program) sees a set of patient cases (patient records) with corresponding diagnoses, it can predict the presence of a disease for future patients. A somewhat unrealistic assumption in typical machine learning applications is that data is freely available. In my dissertation, I will present our research efforts to mitigate this assumption in the areas of active machine learning and budgeted machine learning