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This paper introduces a novel approach to human-machine collaborative learning that allows for the chronically missing human learnability in the context of supervised machine learning. The basic tenet of this approach is the refinement of a human designed software model through the iterative learning loop. Each iteration of the loop consists of two phases: (i) automatic data-driven parameter adjustment, performed by means of stochastic greedy local search, and (ii) human-driven model adjustment based on insights gained in the previous phase. The proposed approach is demonstrated through a real-life study of automatic electricity meter reading in the presence of noise. Thus, a cognitively-inspired non-connectionist approach to digit detection and recognition is introduced, which is subject to refinement through the iterative process of human-machine cooperation. The prototype system is evaluated with respect to the recognition accuracy (with the highest digit recognition accuracy of 94N, and also discussed with respect to the storage requirements, generalizability, utilized contextual information, and efficiency.

Putting Humans Back in the Loop: A Study in Human-Machine Cooperative Learning