Hidden Markov Support Vector Machines
Source: Brown University
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 one calls 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.