In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is
presented as varying along a dimension. The collection of dimensions then form the …

When training a machine learning algorithm for a supervised-learning task in some clinical
applications, uncertainty in the correct labels of some patients may adversely affect the …

This paper proposes a novel active learning method to save annotation effort when
preparing material to train sound event classifiers. K-medoids clustering is performed on …

The mapping relations learning between instances and multiple labels should reflect the
underlying joint probability distribution following by the data sets. The general solution of …

Acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that
develops in patients with critical illnesses including sepsis, pneumonia, and trauma …

ABSTRACT With the Internet of Things paradigm, the data generated by the rapidly
increasing number of connected devices lead to new possibilities, such as using machine …

The small-loss criterion is widely used in recent label-noise learning methods. However,
such a criterion only considers the loss of each training sample in a mini-batch but ignores …

Interactive machine learning (ML) adds a human-in-the-loop aspect to a ML system. Even
though the input from human users to the system is a central part of the concept, the …

The Internet has been witnessing an explosion of video content. According to a Cisco study,
video content accounted for 64% of all the world's internet traffic in 2014, and this …

When building datasets for supervised machine learning problems, data is often labelled
manually by human annotators. In domains like medical imaging, acquiring labels can be …