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Abstract:

Decision forests are an increasingly popular tool in computer vision problems. Their advantages include high computational efficiency, state-of-the-art accuracy and multi-class support. In this paper, we present a novel method for transfer learning which uses decision forests, and we apply it to recognize gestures and characters. We introduce two mechanisms into the decision forest framework in order to transfer knowledge from the source tasks to a given target task. The first one is mixed information gain, which is a data- based regularizer. The second one is label propagation, which infers the manifold structure of the feature space. We show that both of them are important to achieve higher accuracy. Our experiments demonstrate improvements over traditional decision forests in the ChaLearn Gesture Challenge and MNIST data set. They also compare favorably against other state-of-the-art classifiers. © 2014 Norberto A. Goussies, Sebastián Ubalde and Marta Mejail.

Registro:

Documento: Artículo
Título:Transfer learning decision forests for gesture recognition
Autor:Goussies, N.A.; Ubalde, S.; Mejail, M.
Filiación:Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos, Aires Ciudad Autónoma de Buenos Aires, C1428EGA, Argentina
Palabras clave:Decision forests; Gesture recognition; Transfer learning; Computational efficiency; Computer vision; Forestry; Learning algorithms; Computer vision problems; Decision forest; Feature space; Information gain; Label propagation; Manifold structures; Regularizer; Transfer learning; Gesture recognition; Decision Making; Forests
Año:2015
Volumen:15
Página de inicio:3667
Página de fin:3690
Título revista:Journal of Machine Learning Research
Título revista abreviado:J. Mach. Learn. Res.
ISSN:15324435
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15324435_v15_n_p3667_Goussies

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Citas:

---------- APA ----------
Goussies, N.A., Ubalde, S. & Mejail, M. (2015) . Transfer learning decision forests for gesture recognition. Journal of Machine Learning Research, 15, 3667-3690.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15324435_v15_n_p3667_Goussies [ ]
---------- CHICAGO ----------
Goussies, N.A., Ubalde, S., Mejail, M. "Transfer learning decision forests for gesture recognition" . Journal of Machine Learning Research 15 (2015) : 3667-3690.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15324435_v15_n_p3667_Goussies [ ]
---------- MLA ----------
Goussies, N.A., Ubalde, S., Mejail, M. "Transfer learning decision forests for gesture recognition" . Journal of Machine Learning Research, vol. 15, 2015, pp. 3667-3690.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15324435_v15_n_p3667_Goussies [ ]
---------- VANCOUVER ----------
Goussies, N.A., Ubalde, S., Mejail, M. Transfer learning decision forests for gesture recognition. J. Mach. Learn. Res. 2015;15:3667-3690.
Available from: https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15324435_v15_n_p3667_Goussies [ ]