Addressing Bias and Subjectivity in Machine Learning

Zhao, Yijun.

2017

Description
  • Abstract: The success of supervised machine learning algorithms rests on the assumption that data are drawn from the same underlying distribution. However, this assumption is often violated in real world applications where collected data involves human judgement. The contribution of this thesis is a collection of approaches that address bias and subjectivity in real world data. We illustrate our w... read more
This object is in collection Permanent URL
ID:
wh247521p
To Cite:
DCA Citation Guide    EndNote