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Activity recognition with Hidden Markov models using active learning

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3 Author(s)
Hande Alemdar ; Netlab, Bilgisayar Ağları Araştırma Laboratuvarı, Bilgisayar Mühendisliği Bölümü, Boğaziçi Üniversitesi, Turkey ; T. L. M. van Kasteren ; Cem Ersoy

The performance of activity recognition systems depends on annotated training data. Obtaining annotated data is a costly and burdensome task. The need for annotated data for activity recognition systems using Hidden Markov models can be reduced by using active learning methods. Active learning lets the learning algorithm to choose the data from which it learns. In this study, uncertainty sampling methods for active learning are shown to reduce the amount of the needed annotated data in an activity recognition task using real data.

Published in:

2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU)

Date of Conference:

20-22 April 2011