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Generalized Stacked Sequential Learning
Puertas Prats, Eloi

Data: 2015
Resum: In many supervised learning problems, it is assumed that data is independent and identically distributed. This assumption does not hold true in many real cases, where a neighboring pair of examples and their labels exhibit some kind of relationship. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In the literature, there are different approaches that try to capture and exploit this correlation by means of different methodologies. In this thesis we focus on meta-learning strategies and, in particular, the stacked sequential learning (SSL) framework. The main contribution of this thesis is to generalize the SSL highlighting the key role of how to model theneighborhood interactions. We propose an effective and efficient way of capturing and exploiting sequentialcorrelations that take into account long-range interactions. We tested our method on several tasks: text lineclassification, image pixel classification, multi-class classification problems and human pose segmentation. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning as well as off-the-shelf graphical models such conditional random fields.
Nota: Advisor/s: Dr. Oriol Pujol Vila, Dr. Sergio Escalera. Date and location of PhD thesis defense: 14 November 2014, Universitat de Barcelona
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: other ; abstract ; publishedVersion
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 14 Núm. 3 (2015) , p. 24-25 (Special Issue on Recent PhD Thesis Dissemination (2014)) , ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.737

2 p, 1.7 MB

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