Pairwise Cosine Similarity of Emission Probability Matrix as an Indicator of Prediction Accuracy of the Viterbi Algorithm

Guantao Zhao, Ziqiu Zhu, Yinan Sun, Amrinder Arora

2021

Abstract

The Viterbi Algorithm is the main algorithm for the Most Likely Explanation (MLE) used in the HMM. We study the hypothesis that the prediction accuracy of the Viterbi algorithm can be estimated a priori by computing the arithmetic mean of the cosines of the emission probabilities. Our analysis and experimental results suggest a close relationship between these two quantities.

Paper Citation

in Harvard Style

Zhao G., Zhu Z., Sun Y. and Arora A. (2021). Pairwise Cosine Similarity of Emission Probability Matrix as an Indicator of Prediction Accuracy of the Viterbi Algorithm.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 941-946. DOI: 10.5220/0010266509410946

in Bibtex Style

@conference{icaart21,
author={Guantao Zhao and Ziqiu Zhu and Yinan Sun and Amrinder Arora},
title={Pairwise Cosine Similarity of Emission Probability Matrix as an Indicator of Prediction Accuracy of the Viterbi Algorithm},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={941-946},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010266509410946},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Pairwise Cosine Similarity of Emission Probability Matrix as an Indicator of Prediction Accuracy of the Viterbi Algorithm
SN - 978-989-758-484-8
AU - Zhao G.
AU - Zhu Z.
AU - Sun Y.
AU - Arora A.
PY - 2021
SP - 941
EP - 946
DO - 10.5220/0010266509410946