Interactive Machine Learning (IML) seeks to complement human perception and intelligence
by tightly integrating these strengths with the computational power and speed of computers …

Intelligent systems that learn interactively from their end-users are quickly becoming
widespread. Until recently, this progress has been fueled mostly by advances in machine …

End-user interactive machine learning is a promising tool for enhancing human capabilities
with large data. Recent work has shown that we can create end-user interactive machine …

Human-centered approaches to machine learning have established theoretical foundations,
design principles and interaction techniques to facilitate end-user interaction with machine …

Interactive machine learning (IML) is an iterative learning process that tightly couples a
human with a machine learner, which is widely used by researchers and practitioners to …

Model evaluation plays a special role in interactive machine learning (IML) systems in which
users rely on their assessment of a model's performance in order to determine how to …

Perceptual user interfaces (PUIs) are an important part of ubiquitous computing. Creating
such interfaces is difficult because of the image and signal processing knowledge required …

Interactive user interfaces need to continuously evolve based on the interactions that a user
has (or does not have) with the system. This may require constant exploration of various …

In this paper we examine the notion of adaptive user interfaces, interactive systems that
invoke machine learning to improve their interaction with humans. We review some previous …

In this paper we examine the growing interest in personalized user interfaces and explore
the potential of machine learning in meeting that need. We briefly review progress in …