2026-04-19T16:14:52Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/212082026-02-06T02:21:34Zcom_2072_1033col_2072_452950

Spectral learning of sequence taggers over continuous sequences Recasens, Adria Quattoni, Ariadna Julieta Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Data mining Mineria de dades 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 combinations of elementary transitions and the weights of the linear combination are determined by dynamic features of the continuous input sequence. The resulting learning algorithm is efficient and accurate. Peer Reviewed Postprint (published version) 2013 Conference report 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. 978-364240987-5 https://hdl.handle.net/2117/21208 10.1007/978-3-642-40988-2_19 eng http://link.springer.com/chapter/10.1007/978-3-642-40988-2_19 Restricted access - publisher's policy 16 p. application/pdf Springer-Verlag