Spectral learning of sequence taggers over continuous sequences
Document typeConference report
Rights accessRestricted access - publisher's policy
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.
CitationRecasens, 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.