Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Expert Systems with Applications Year : 2013

Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile

Abstract

In this work we apply multi-class support vector machines (SVM) and a multi-class stochastic SVM formulation to the classification of fish schools of three species: anchovy, common sardine, and jack mackerel, and we compare their performance. The data used come from acoustic measurements in southern-central Chile. These classifications were carried out by using a diver set of descriptors including morphology, bathymetry, energy, and space positions. In both type of formulations, the deterministic and the stochastic one, the strategy used to classify multi-class SVM consists in employing the criterion one-species-against-the-Rest. We thus provide an empirical way to adjust the parameters involved in the stochastic classifiers with the aim of improving its performance. When this procedure is applied to the classification of fish schools we obtain a classifier with a better performance than the deterministic classifier.
Fichier principal
Vignette du fichier
SVMR_peces 05Dic2012.pdf (300.22 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01088228 , version 1 (27-11-2014)

Identifiers

Cite

Paul Bosch, Julio López, Héctor Ramírez, Hugo Robotham. Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile. Expert Systems with Applications, 2013, 40, pp.4029 - 4034. ⟨10.1016/j.eswa.2013.01.006⟩. ⟨hal-01088228⟩
149 View
372 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More