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Spectral learning of sequence taggers over continuous sequences

Autor
Recasens, A.; Quattoni, A.J.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
ECML 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Any de l'edició
2013
Data de presentació
2013-09
Llibre d'actes
Machine Learning and Knowledge Discovery in Databases European Conference : ECML PKDD 2013, Proceedings, Part I
Pàgina inicial
289
Pàgina final
304
Editor
Springer-Verlag
DOI
https://doi.org/10.1007/978-3-642-40988-2_19 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/21208 Obrir en finestra nova
URL
http://link.springer.com/chapter/10.1007/978-3-642-40988-2_19 Obrir en finestra nova
Resum
In this paper we present a spectral algorithm for learning weighted finite-state sequence taggers (WFSTs) over paired input-output sequences, where the input is continuous and the output discrete. WFSTs are an important tool for modelling paired input-output sequences and have numerous applications in real-world problems. Our approach is based on generalizing the class of weighted finite-state sequence taggers over discrete input-output sequences to a class where transitions are linear combinati...
Citació
Recasens, A.; Quattoni, A.J. Spectral learning of sequence taggers over continuous sequences. A: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. "Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I". Praga: Springer-Verlag, 2013, p. 289-304.
Grup de recerca
LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge

Participants

  • Recasens, Adria  (autor ponent)
  • Quattoni, Ariadna Julieta  (autor ponent)