CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning
Abstract
The growth of information available to learning systems and the increasing complexity of learning tasks determine the need for devising algorithms that scale well with respect to all learning parameters. In the context of supervised sequential learning, the Viterbi algorithm plays a fundamental role, by allowing the evaluation of the best (most probable) sequence of labels with a time complexity linear in the number of time events, and quadratic in the number of labels.
Cite
Text
Esposito and Radicioni. "CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning." Journal of Machine Learning Research, 2009.Markdown
[Esposito and Radicioni. "CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/esposito2009jmlr-carpediem/)BibTeX
@article{esposito2009jmlr-carpediem,
title = {{CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning}},
author = {Esposito, Roberto and Radicioni, Daniele P.},
journal = {Journal of Machine Learning Research},
year = {2009},
pages = {1851-1880},
volume = {10},
url = {https://mlanthology.org/jmlr/2009/esposito2009jmlr-carpediem/}
}