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Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data

  • Published:2023-04 Issue: Volume:180 Page:107663
  • ISSN:0167-9473
  • Container-title:Computational Statistics & Data Analysis
  • language:en
  • Short-container-title:Computational Statistics & Data Analysis

Author:

Gangloff Hugo,Morales Katherine,Petetin Yohan

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Statistics and Probability

Reference64 articles.

1. Exploring convolutional neural network structures and optimization techniques for speech recognition;Abdel-Hamid,2013

2. Statistical guarantees for the EM algorithm: from population to sample-based analysis;Balakrishnan;Ann. Stat.,2017

3. Dempster–Shafer fusion of evidential pairwise Markov chains;Boudaren;IEEE Trans. Fuzzy Syst.,2016

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1. A Markov Switching Autoregressive Model with Time-Varying Parameters;Forecasting;2024-07-29

2. Time inhomogeneous multivariate Markov chains: Detecting and testing multiple structural breaks occurring at unknown dates;Chaos, Solitons & Fractals;2024-03

3. Non-stationary data segmentation with hidden evidential semi-Markov chains;International Journal of Approximate Reasoning;2023-11

4. Forecasting with Pairwise Gaussian Markov Models;2023 8th International Conference on Mathematics and Computers in Sciences and Industry (MCSI);2023-10-14

5. A Probabilistic Semi-Supervised Approach with Triplet Markov Chains;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17
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