Quadratic Weighted Automata:Spectral Algorithm and Likelihood Maximization
Abstract
In this paper, we address the problem of non-parametric density estimation on a set of strings $\Sigma^*$. We introduce a probabilistic model - called quadratic weighted automaton, or QWA - and we present some methods which can be used in a density estimation task. A spectral analysis method leads to an effective regularization and a consistent estimate of the parameters. We provide a set of theoretical results on the convergence of this method. Experiments show that the combination of this method with likelihood maximization may be an interesting alternative to the well-known Baum-Welch algorithm.
Cite
Text
Bailly. "Quadratic Weighted Automata:Spectral Algorithm and Likelihood Maximization." Proceedings of the Third Asian Conference on Machine Learning, 2011.Markdown
[Bailly. "Quadratic Weighted Automata:Spectral Algorithm and Likelihood Maximization." Proceedings of the Third Asian Conference on Machine Learning, 2011.](https://mlanthology.org/acml/2011/bailly2011acml-quadratic/)BibTeX
@inproceedings{bailly2011acml-quadratic,
title = {{Quadratic Weighted Automata:Spectral Algorithm and Likelihood Maximization}},
author = {Bailly, Raphael},
booktitle = {Proceedings of the Third Asian Conference on Machine Learning},
year = {2011},
pages = {147-163},
volume = {20},
url = {https://mlanthology.org/acml/2011/bailly2011acml-quadratic/}
}