Inductive Inference of First-Order Models from Numeric-Symbolic Data
Abstract
A factor common to statistical techniques of data analysis is the adopted representation formalism: A tabular (zeroth-order) model with almost exclusively numerical features . On the contrary, several studies on machine learning concern the induction of first-order models from symbolic data, but are inadequate for continuous data. In the paper, we face the problem of handling both numerical and symbolic data in first-order models. distinguishing the moment of model generation from examples (induction) from the moment of model recognition by means of a flexible. probabilistic subsumption test. We demonstrate the proposed solutions on a problem in document understanding where the objective is to induce the models of the logical structure of some real business letters.
Cite
Text
Esposito et al. "Inductive Inference of First-Order Models from Numeric-Symbolic Data." Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997.Markdown
[Esposito et al. "Inductive Inference of First-Order Models from Numeric-Symbolic Data." Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997.](https://mlanthology.org/aistats/1997/esposito1997aistats-inductive/)BibTeX
@inproceedings{esposito1997aistats-inductive,
title = {{Inductive Inference of First-Order Models from Numeric-Symbolic Data}},
author = {Esposito, Floriana and Caggese, Sergio and Malerba, Donato and Semeraro, Giovanni},
booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics},
year = {1997},
pages = {173-182},
volume = {R1},
url = {https://mlanthology.org/aistats/1997/esposito1997aistats-inductive/}
}