Identifying and Using Patterns in Sequential Data
Abstract
Whereas basic machine learning research has mostly viewed input data as an unordered random sample from a population, researchers have also studied learning from data whose input sequence follows a regular sequence. To do so requires that we regard the input data as a stream and identify regularities in the data values as they occur. In this brief survey I review three sequential-learning problems, examine some new, and not-so-new, algorithms for learning from sequences, and give applications for these methods. The three generic problems I discuss are: Predicting sequences of discrete symbols generated by stochastic processes. Learning streams by extrapolation from a general rule. Learning to predict time series.
Cite
Text
Laird. "Identifying and Using Patterns in Sequential Data." International Conference on Algorithmic Learning Theory, 1993. doi:10.1007/3-540-57370-4_33Markdown
[Laird. "Identifying and Using Patterns in Sequential Data." International Conference on Algorithmic Learning Theory, 1993.](https://mlanthology.org/alt/1993/laird1993alt-identifying/) doi:10.1007/3-540-57370-4_33BibTeX
@inproceedings{laird1993alt-identifying,
title = {{Identifying and Using Patterns in Sequential Data}},
author = {Laird, Philip},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {1993},
pages = {1-18},
doi = {10.1007/3-540-57370-4_33},
url = {https://mlanthology.org/alt/1993/laird1993alt-identifying/}
}