Abstract: Humans may be exceptional learners but
they have biological limitations and moreover,
inductive biases similar to machine
learning algorithms. This puts limits on
human learning ability and on the kinds
of learning tasks humans can easily handle.
In this paper, we consider the problem
of boosting” human learners to extend
the learning ability of human learners and
achieve improved performance on tasks which
individual humans nd dicult. We consider
classi cation (category learning) tasks, propose
a boosting algorithm for human learners
and give theoretical justi cations. We
conduct experiments using Amazon’s Mechanical
Turk on two synthetic datasets {
a crosshair task with a nonlinear decision
boundary and a gabor patch task with a linear
boundary but which is inaccessible to human
learners { and one real world dataset {
the Opinion Spam detection task introduced
in (Ott et al., 2011). Our results show that
boosting human learners produces gains in
accuracy and can overcome some fundamental
limitations of human learners.
- Topics:
- Crowd Computing
Download: pdf
Citation
- Human Boosting (pdf, software)
H. Pareek, P. Ravikumar.
In International Conference on Machine Learning (ICML), 2013.
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