Challenges in Interactive Machine Learning

Machine Learning for Interactive Systems: Challenges and Future Trends

Machine learning has been introduced more than 40 years ago in interactive systems through speech recognition or computer vision. Since that, machine learning gained in interest in the scientific community involved in humanmachine interaction and raised in the abstraction scale. It moved from fundamental signal processing to language understanding and generation, emotion and mood recognition an...

Challenges in Statistical Machine Learning

Interactive machine learning using BIDMach

Machine learning is growing in importance in industry, the sciences, and many other fields. In many and perhaps most of these applications, users need to trade off competing goals and build different model prototypes rapidly, which requires much human intelligence and is time consuming. Therefore, interactive customization and optimization aims to help expert incorporate secondary criteria into...

An Interactive Machine Learning Framework

Machine learning (ML) is believed to be an effective and efficient tool to build reliable prediction model or extract useful structure from an avalanche of data. However, ML is also criticized by its difficulty in interpretation and complicated parameter tuning. In contrast, visualization is able to well organize and visually encode the entangled information in data and guild audiences to simpl...

Challenges and Opportunities in Applied Machine Learning

and generating land-cover maps of the Earth have in common? Both are real-world problems for which we have applied machine-learning techniques to assist human experts, and in each case doing so has motivated the development of novel machine-learning methods. Our research group works closely with domain experts from other disciplines to solve practical problems. For many tasks, off-the-shelf met...