Engineering Applications of Artificial Intelligence, ( ISI ), Volume (85), Year (2019-10) , Pages (21-32)

Title : ( Functional gradient approach to probabilistic minimax active learning )

Authors: Hossein Ghafarian , Hadi Sadoghi Yazdi ,

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Many active learning methods select informative and representative examples by employing a parametric approach. However, there is very limited research pursuing a functional non-parametric approach to informative and representative active learning. The present study proposes a general functional approach to active learning based on a minimax objective function. Using this general algorithm, the current paper presents a specific algorithm based on a simple class of functions. Experiments show that the proposed method is efficient in selecting examples. It is interesting that the resulting algorithm can be interpreted from a spectral filtering perspective. This establishes a relationship between active learning, boosting, and spectral filtering and opens up new avenues for developing even better active learning algorithms.


, Active learning, Functional gradient, Probabilistic minimax active learning, Boosting
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author = {Ghafarian, Hossein and Sadoghi Yazdi, Hadi},
title = {Functional gradient approach to probabilistic minimax active learning},
journal = {Engineering Applications of Artificial Intelligence},
year = {2019},
volume = {85},
month = {October},
issn = {0952-1976},
pages = {21--32},
numpages = {11},
keywords = {Active learning;Functional gradient; Probabilistic minimax active learning; Boosting},


%0 Journal Article
%T Functional gradient approach to probabilistic minimax active learning
%A Ghafarian, Hossein
%A Sadoghi Yazdi, Hadi
%J Engineering Applications of Artificial Intelligence
%@ 0952-1976
%D 2019