Generalization Ability of a Support Vector Classifier Applied to Vehicle Data in a Microphone Network

Lauberts, A.; Lindgren, David

doi:10.1109/icif.2006.301636

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…In [3], ground vehicles are identified by using microphones and signal processing methods similar to the ones used in this work. Low-frequency spectral features are used by a support vector machine classifier to determine the model of passing vehicles.…”

Section: A Outlinementioning

confidence: 99%

Surface Ship Classification in a Littoral Environment using Fusion of Hydroacoustic and Electromagnetic Data

et al. 2006

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In this paper we investigate if underwater electrode arrays can complement hydrophones in passive surveillance situations with occasionally poor hydroacoustic conditions. The application is classification of passing surface ships as either light or heavy. Different signal processing and fusion techniques are compared in conjunction with a support vector machine classifier. It is shown that in some situations, the fusion of electrode and hydroacoustic data actually improves the performance of the passive surveillance system. At best, our system correctly classifies 22 ship passages out of 23 possible.

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Section: A Outlinementioning

confidence: 99%

Surface Ship Classification in a Littoral Environment using Fusion of Hydroacoustic and Electromagnetic Data

et al. 2006

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“…• According to the decision functions, [8] defined a probability (19) in order to normalize the decision functions. Hence, we can combine the binary classifiers (for both one-versus-rest and one-versus-one cases) with a Bayesian rule (see [15]) or with more simple rules (see [7]). • DAGSVM (Directed Acyclic Graph SVM) proposed by [16]: In this approach, the learning is made as the oneversus-one with the learning of n(n−1)/2 binary decision functions.…”

Section: B Multi-class Classification With Svmmentioning

confidence: 99%

Decision Support with Belief Functions Theory for Seabed Characterization

Martin,

Quidu

2008

Preprint

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The seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as one-versusone or one-versus-rest, usually used in the SVM-classification. The decision functions coming from these binary classifiers are interpreted in terms of belief functions in order to combine these functions with one of the numerous operators of the belief functions theory. Moreover, this interpretation of the decision function allows us to propose a process of decisions by taking into account the rejected observations too far removed from the learning data, and the imprecise decisions given in unions of classes. This new approach is illustrated and evaluated with a SVM in order to classify the different kinds of sediment on image sonar.

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Generalization Ability of a Support Vector Classifier Applied to Vehicle Data in a Microphone Network

Cited by 2 publications

References 3 publications

Surface Ship Classification in a Littoral Environment using Fusion of Hydroacoustic and Electromagnetic Data

Surface Ship Classification in a Littoral Environment using Fusion of Hydroacoustic and Electromagnetic Data

Decision Support with Belief Functions Theory for Seabed Characterization

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