51,889 research outputs found
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems
This paper addresses an important issue, known as sensor drift that behaves a
nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of
machine learning. Traditional methods for drift compensation are laborious and
costly due to the frequent acquisition and labeling process for gases samples
recalibration. Extreme learning machines (ELMs) have been confirmed to be
efficient and effective learning techniques for pattern recognition and
regression. However, ELMs primarily focus on the supervised, semi-supervised
and unsupervised learning problems in single domain (i.e. source domain). To
our best knowledge, ELM with cross-domain learning capability has never been
studied. This paper proposes a unified framework, referred to as Domain
Adaptation Extreme Learning Machine (DAELM), which learns a robust classifier
by leveraging a limited number of labeled data from target domain for drift
compensation as well as gases recognition in E-nose systems, without loss of
the computational efficiency and learning ability of traditional ELM. In the
unified framework, two algorithms called DAELM-S and DAELM-T are proposed for
the purpose of this paper, respectively. In order to percept the differences
among ELM, DAELM-S and DAELM-T, two remarks are provided. Experiments on the
popular sensor drift data with multiple batches collected by E-nose system
clearly demonstrate that the proposed DAELM significantly outperforms existing
drift compensation methods without cumbersome measures, and also bring new
perspectives for ELM.Comment: 11 pages, 9 figures, to appear in IEEE Transactions on
Instrumentation and Measuremen
Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data
Knowledge about the culture of a user is especially important for the design
of e-learning applications. In the experiment reported here, questionnaire
data was used to build machine learning models to automatically predict the
culture of a user. This work can be applied to automatic culture detection
and subsequently to the adaptation of user interfaces in e-learning
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