The focus of the CHIL group is to make classifiers and other Machine Learning (ML) artefacts easy to create, update, and transfer with little ML expertise and negligible cost.
- ICE: Interative Classification and Entity ExtractionQuick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The system leverages big data to find examples that maximize the training value of its interaction with the teacher.
- Denis Charles
- David Grangier
Simard, P., Chickering, D., Lakshmiratan, A., Charles, D., Bottou, L., Suarez, C.G.J., Grangier,D., Amershi,S., Verwey,J., Suh,J.: Ice: Enabling non-experts to build models interactively for large-scale lopsided problems. arXiv:1409.4814 [cs.AI], Microsoft Research (2014)
- Jason D. Williams, Nobal B. Niraula, Pradeep Dasigi, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez, Mouni Reddy, and Geoff Zweig, Rapidly scaling dialog systems with interactive learning, 11 January 2015