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Title: A conditional random field-based model for joint sequence segmentation and classification
Authors: Kosmopoulos, Dimitrios 
Doliotis, Paul 
Chatzis, Sotirios P. 
Keywords: Computer science;Pattern recognition;Signal processing
Category: Computer and Information Sciences
Field: Engineering and Technology
Issue Date: 2013
Publisher: Elsevier
Source: Pattern recognition, 2013, vol. 46, no. 6, pp. 1569–1578
Journal: Pattern recognition 
Abstract: In this paper, we consider the problem of joint segmentation and classification of sequences in the framework of conditional random field (CRF) models. To effect this goal, we introduce a novel dual-functionality CRF model: on the first level, the proposed model conducts sequence segmentation, whereas, on the second level, the whole observed sequences are classified into one of the available learned classes. These two procedures are conducted in a joint, synergetic fashion, thus optimally exploiting the information contained in the used model training sequences. Model training is conducted by means of an efficient likelihood maximization algorithm, and inference is based on the familiar Viterbi algorithm. We evaluate the efficacy of our approach considering a real-world application, and we compare its performance to popular alternatives
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2012.11.028
Rights: © 2012 Elsevier
Type: Article
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