Refereed full papers (journals, book chapters, international conferences)

2004

@article{BMF2004,
	vgclass =	{refpap},
	author =	{Manuele Bicegoa and Vittorio Murinoa and M\`{a}rio A. T.
	Figueiredo},
	title =	{Similarity-based classification of sequences using hidden
	{M}arkov models},
	journal =	{Pattern Recognition},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.patcog.2004.04.005},
	abstract =	{Hidden Markov models (HMM) are a widely used tool for
	sequence modelling. In the sequence classification case, the standard
	approach consists of training one HMM for each class and then using a
	standard Bayesian classification rule. In this paper, we introduce a
	novel classification scheme for sequences based on HMMs, which is
	obtained by extending the recently proposed similarity-based
	classification paradigm to HMM-based classification. In this approach,
	each object is described by the vector of its similarities with respect
	to a predetermined set of other objects, where these similarities are
	supported by HMMs. A central problem is the high dimensionality of
	resulting space, and, to deal with it, three alternatives are
	investigated. Synthetic and real experiments show that the
	similarity-based approach outperforms standard HMM classification
	schemes.},
}