Semi-Supervised Nearest Mean Classification Through a Constrained Log-Likelihood

doi:10.1109/TNNLS.2014.2329567

2015-05-01

PRE

Marco Loog *

Are Charles Jensen

DOI：10.1109/TNNLS.2014.2329567

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摘要

En 中文

We cast a semi-supervised nearest mean classifier, previously introduced by the first author, in a more principled log-likelihood formulation that is subject to constraints. This, in turn, leads us to make the important suggestion to not only investigate error rates of semi-supervised learners but also consider the risk they originally aim to optimize. We demonstrate empirically that in terms of classification error, mixed results are obtained when comparing supervised to semi-supervised nearest mean classification, while in terms of log-likelihood on the test set, the semi-supervised method consistently outperforms its supervised counterpart. Comparisons to self-learning, a standard approach in semi-supervised learning, are included to further clarify the way, in which our constrained nearest mean classifier improves over regular, supervised nearest mean classification.

Keyword:

Constrained estimation

inherent loss

log-likelihood

nearest centroid

nearest mean classifier

semi-supervised learning

surrogate loss

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期刊

IEEE Transactions on Neural Networks and Learning Systems

IF:

8.9

论文数:

7.6K

被引数:

7.2W

机构

Delft University of Technology

学者数:

2.6W

论文数: 2.5W

被引数: 3.8W

university of oslo

学者数:

4.2W

论文数: 3.5W

被引数: 53

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