Learning by Combining Observations and User Edits

Abstract

We introduce a new collaborative machine learning paradigm in which the user directs a learning algorithm by manually editing the automatically induced model. We identify a generic architecture that supports seam-less interweaving of automated learning from training samples and manual edits of the model, and we dis-cuss the main difficulties that the framework addresses. We describe Augmentation-Based Learning (ABL), the first learning algorithm that supports interweaving of edits and learning from training samples. We use exam-ples based on ABL to outline selected advantages of the approach—dealing with bad data by manually remov-ing their effects from the model, and learning a model with fewer training samples.