Distributed processing of biosignal-database for emotion recognition with mahout
V Kollia, OH Elibol - arXiv preprint arXiv:1609.02631, 2016 - arxiv.org
V Kollia, OH Elibol
arXiv preprint arXiv:1609.02631, 2016•arxiv.orgThis paper investigates the use of distributed processing on the problem of emotion
recognition from physiological sensors using a popular machine learning library on
distributed mode. Specifically, we run a random forests classifier on the biosignal-data,
which have been pre-processed to form exclusive groups in an unsupervised fashion, on a
Cloudera cluster using Mahout. The use of distributed processing significantly reduces the
time required for the offline training of the classifier, enabling processing of large …
recognition from physiological sensors using a popular machine learning library on
distributed mode. Specifically, we run a random forests classifier on the biosignal-data,
which have been pre-processed to form exclusive groups in an unsupervised fashion, on a
Cloudera cluster using Mahout. The use of distributed processing significantly reduces the
time required for the offline training of the classifier, enabling processing of large …
This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode. Specifically, we run a random forests classifier on the biosignal-data, which have been pre-processed to form exclusive groups in an unsupervised fashion, on a Cloudera cluster using Mahout. The use of distributed processing significantly reduces the time required for the offline training of the classifier, enabling processing of large physiological datasets through many iterations.
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