Abstract
We propose a strategy for semi-supervised learning of Hidden-state Conditional Random Fields (HCRF) for signal classification. It builds on simple procedures for semi-supervised learning of Hidden Markov Models (HMM) and on strategies for learning a HCRF from a trained HMM system. The algorithm learns a generative system based on Hidden Markov models and a discriminative one based on HCRFs where each model is refined by the other in an iterative framework.
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Soullard, Y., Artieres, T. (2012). Iterative Refinement of HMM and HCRF for Sequence Classification. In: Schwenker, F., Trentin, E. (eds) Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science(), vol 7081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28258-4_10
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DOI: https://doi.org/10.1007/978-3-642-28258-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28257-7
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