Hidden Markov Support Vector Machines

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Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine. The proposed architecture handles dependencies between neighboring labels using Viterbi decoding. In contrast to standard HMM training, the learning procedure is discriminative and is based on a maximum/soft margin criterion. Compared to previous methods like Conditional Random Fields, Maximum Entropy Markov Models and label sequence boosting, HM-SVMs have a number of advantages. Most notably, it is possible to learn non-linear discriminant functions via kernel functions. At the same time, HM-SVMs share the key advantages with other discriminative methods, in particular the capability to deal with overlapping features. We report experimental evaluations on two tasks, named entity recognition and part-of-speech tagging, that demonstrate...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2003
Where ICML
Authors Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann
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