Maximum margin hidden Markov models for sequence classification
| Autor: | Nikolaus Mutsam, Franz Pernkopf |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Maximum likelihood Maximum-entropy Markov model Speech recognition Probabilistic logic 020206 networking & telecommunications Pattern recognition 02 engineering and technology Mixture model Generative model ComputingMethodologies_PATTERNRECOGNITION Discriminative model Artificial Intelligence Margin (machine learning) Signal Processing Hinge loss 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Hidden Markov model business Software Discriminative learning |
| Zdroj: | Pattern Recognition Letters. 77:14-20 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2016.03.017 |
| Popis: | A margin-based discriminative training method for hidden Markov models is proposed.The margin criterion is maximized using the extended Baum-Welch algorithm.Classification rate for time series data is on par with state of the art methods. Discriminative learning methods are known to work well in pattern classification tasks and often show benefits compared to generative learning. This is particularly true in case of model mismatch, i.e. the model cannot represent the true data distribution. In this paper, we derive discriminative maximum margin learning for hidden Markov models (HMMs) with emission probabilities represented by Gaussian mixture models (GMMs). The focus is on single-label sequence classification where the margin objective is specified by the probabilistic gap between the true class and the most competing class. In particular, we use the extended Baum-Welch (EBW) framework to optimize this probabilistic margin embedded in a hinge loss function. Approximations of the margin objective and the derivatives are necessary. In the experiments, we compare maximum margin HMMs to generative maximum likelihood and discriminative conditional log-likelihood (CLL) HMM training. We present results of classifying trajectories of handwritten characters, Australian sign language data, digits of speech data and UCR time-series data. Maximum margin HMMs outperform in many cases CLL-HMMs. Furthermore, maximum margin HMMs achieve a significantly better performance than generative maximum likelihood HMMs. |
| Databáze: | OpenAIRE |
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