Graph-regularized multi-class support vector machines for face and action recognition

Iosifidis, Alexandros; Gabbouj, Moncef

doi:10.1109/eusipco.2016.7760217

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Machine Learning is a field of Artificial Intelligence that aims to build a model that separates data into various groups, referred as classes. Those methods are relatively new but currently widely spread [13], [14] and used in a wide range of applications including medical imaging [15] or image processing [16], [13] or hyperspectral imaging. Supervised Machine Learning refers to a set of methods that build a prediction model from a labeled dataset: set of observations whose thematic class is known (usually after a manual labeling).…”

Section: Proposed Architecture For Automatic Classification a Smentioning

confidence: 99%

Machine learning for automatic classification of volcano-seismic signatures

Malfante¹,

Mura²,

Mars³

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The evaluation and prediction of volcanoes activities and associated risks is still a timely and open issue. The amount of volcano-seismic data acquired by recent monitoring stations is huge (e.g., several years of continuous recordings), thereby making machine learning absolutely necessary for their automatic analysis. The transient nature of the volcano-seismic signatures of interest further enforces the need of automatic detection and classification of such events. In this paper, we present a novel architecture for automatic classification of volcano-seismic events based on a comprehensive signal representation with a large feature set. To the best of our knowledge this is one of the first attempts to automatize the classification task of these signals. The proposed approach relies on supervised machine learning techniques to build a prediction model.

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