کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
535098 870320 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Maximum margin hidden Markov models for sequence classification
ترجمه فارسی عنوان
مدل های مخفی مارکوف حاشیه ای حداکثری برای طبقه بندی توالی
کلمات کلیدی
مدل مخفی مارکوف؛ یادگیری تبعیض آمیز؛ حداکثر یادگیری حاشیه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 77, 1 July 2016, Pages 14–20
نویسندگان
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