Modeling and Clustering the Behavior of Animals Using Hidden Markov Models.

Autoři
Šabata, T.; Borovička, T.; Holeňa, M.
Rok
2016
Publikováno
Proceedings ITAT 2016: Information Technologies - Applications and Theory.. Luxemburg: CreateSpace Independent Publishing Platform, 2016. p. 172-178. ISBN 978-1-5370-1674-0.
Typ
Stať ve sborníku

K-best Viterbi Semi-supervized Active Learning in Sequence Labelling

Autoři
Šabata, T.; Borovička, T.; Holeňa, M.
Rok
2017
Publikováno
CEUR workshop proceedings. 2017, 2017 144-152. ISSN 1613-0073.
Typ
Článek
Anotace
In application domains where there exists a large amount of unlabelled data but obtaining labels is expensive, active learning is a useful way to select which data should be labelled. In addition to its traditional successful use in classification and regression tasks, active learning has been also applied to sequence labelling. According to the standard active learning approach, sequences for which the labelling would be the most informative should be labelled. However, labelling the entire sequence may be inefficient as for some its parts, the labels can be predicted using a model. Labelling such parts brings only a little new information. Therefore in this paper, we investigate a sequence labelling approach in which in the sequence selected for labelling, the labels of most tokens are predicted by a model and only tokens that the model can not predict with sufficient confidence are labelled. Those tokens are identified using the k-best Viterbi algorithm.

Za obsah stránky zodpovídá: doc. Ing. Štěpán Starosta, Ph.D.