.png){kind=icon}
Return
Maximum margin hidden Markov models for sequence classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2016.03.017.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Hidden Markov models
Discriminative learning
Maximum margin learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
8.0K
Citations:
1.6W
Organization
Cited Papers
A TUTORIAL ON HIDDEN MARKOV-MODELS AND SELECTED APPLICATIONS IN SPEECH RECOGNITION.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
ShareSaveShareSaveShareSave
ShareSaveShareSaveShareSave
View more