General Supervision via Probabilistic Transformations
This addresses the challenge of integrating varied training data schemes in machine learning, though it appears incremental as it builds on existing supervision methods.
The paper tackles the problem of supervised classification with diverse training data types by introducing a unifying framework and generalized robust risk minimization (GRRM), enabling handling of general ensembles of training data in a unified manner and supporting new supervision schemes.
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training data. This paper presents a unifying framework for supervised classification with general ensembles of training data, and proposes the learning methodology of generalized robust risk minimization (GRRM). The paper shows how current and novel supervision schemes can be addressed under the proposed framework by representing the relationship between examples at test and training via probabilistic transformations. The results show that GRRM can handle different types of training data in a unified manner, and enable new supervision schemes that aggregate general ensembles of training data.