caa a22 4500 445871342 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20110901xx s 000 0 eng 10.1007/s10618-010-0206-6 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0206-6 Sequence classification via large margin hidden Markov models [Elektronische Daten] [Minyoung Kim, Vladimir Pavlovic] We address the sequence classification problem using a probabilistic model based on hidden Markov models (HMMs). In contrast to commonly-used likelihood-based learning methods such as the joint/conditional maximum likelihood estimator, we introduce a discriminative learning algorithm that focuses on class margin maximization. Our approach has two main advantages: (i) As an extension of support vector machines (SVMs) to sequential, non-Euclidean data, the approach inherits benefits of margin-based classifiers, such as the provable generalization error bounds. (ii) Unlike many algorithms based on non-parametric estimation of similarity measures that enforce weak constraints on the data domain, our approach utilizes the HMM's latent Markov structure to regularize the model in the high-dimensional sequence space. We demonstrate significant improvements in classification performance of the proposed method in an extensive set of evaluations on time-series sequence data that frequently appear in data mining and computer vision domains. The Author(s), 2010 Sequence classification nationallicence Hidden Markov models nationallicence Kim Minyoung Department of Electronic & Information Engineering, Seoul National University of Science & Technology, 139-743, Seoul, Korea aut Pavlovic Vladimir Department of Computer Science, Rutgers University, Piscataway, NJ, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/2(2011-09-01), 322-344 1384-5810 23:2<322 2011 23 10618 https://doi.org/10.1007/s10618-010-0206-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0206-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kim Minyoung Department of Electronic & Information Engineering, Seoul National University of Science & Technology, 139-743, Seoul, Korea aut NATIONALLICENCE 700 1- Pavlovic Vladimir Department of Computer Science, Rutgers University, Piscataway, NJ, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/2(2011-09-01), 322-344 1384-5810 23:2<322 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer