Discriminatively trained Markov model for sequence classification

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GeDi: Generative Discriminator Guided Sequence Generation (2020, methodology): “The idea of discriminatively training class-conditional generative models has previously been considered for the classification of text (Yakhnenko et al., 2005), and images (Lasserre et al.”
A brief survey on sequence classification (2010, methodology): “[64] apply a k-order Markov model to classify protein and text sequence data.”
Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models (2015, unknown): “, [7,8]), or Recurrent Neural Networks (e.”
Principled Hybrids of Generative and Discriminative Models (2006, background): “In particular, there has been much interest in ‘discriminative training’ of generative models [2, 3, 12 ] with a view to improving classification accuracy.,In order to improve the predictive performance of generative models it has been proposed to use ‘discriminative training’ [ 12 ] which involves”