Grammatical inference in document recognition

1998 ◽  
pp. 175-186 ◽  
Author(s):  
Alexander S. Saidi ◽  
Souad Tayeb-bey
Keyword(s):  
Grammatical Inference ◽  
Document Recognition

An automated system for document recognition

1989 ◽  
Vol 2 (2) ◽  
pp. 120-130
Author(s):  
Wei-Chung Lin ◽  
Yu-Jen Eugene Feng
Keyword(s):  
Automated System ◽  
Document Recognition

Grammatical Inference

2017 ◽  
pp. 569-570
Author(s):  
Lorenza Saitta ◽  
Michele Sebag
Keyword(s):  
Grammatical Inference

Grammatical Inference

2011 ◽  
pp. 458-458
Author(s):  
Xinhua Zhang ◽  
Novi Quadrianto ◽  
Kristian Kersting ◽  
Zhao Xu ◽  
Yaakov Engel ◽  
...  
Keyword(s):  
Grammatical Inference

scholarly journals Parsing Expression Grammars and Their Induction Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8747
Author(s):  
Wojciech Wieczorek ◽  
Olgierd Unold ◽  
Łukasz Strąk
Keyword(s):  
Genetic Programming ◽  
Roc Curve ◽  
Classification Performance ◽  
Grammatical Inference ◽  
Inference Algorithm ◽  
Finite Sample ◽  
Induction Method ◽  
Grammar Learning ◽  
Area Under Roc Curve ◽  
Inference Methods

Grammatical inference (GI), i.e., the task of finding a rule that lies behind given words, can be used in the analyses of amyloidogenic sequence fragments, which are essential in studies of neurodegenerative diseases. In this paper, we developed a new method that generates non-circular parsing expression grammars (PEGs) and compares it with other GI algorithms on the sequences from a real dataset. The main contribution of this paper is a genetic programming-based algorithm for the induction of parsing expression grammars from a finite sample. The induction method has been tested on a real bioinformatics dataset and its classification performance has been compared to the achievements of existing grammatical inference methods. The evaluation of the generated PEG on an amyloidogenic dataset revealed its accuracy when predicting amyloid segments. We show that the new grammatical inference algorithm achieves the best ACC (Accuracy), AUC (Area under ROC curve), and MCC (Mathew’s correlation coefficient) scores in comparison to five other automata or grammar learning methods.

scholarly journals Document Recognition for a Million Books

2006 ◽  
Vol 12 (3) ◽  
Author(s):  
G. Sayeed Choudhury ◽  
Tim DiLauro ◽  
Robert Ferguson ◽  
Michael Droettboom ◽  
Ichiro Fujinaga
Keyword(s):  
Document Recognition

scholarly journals Grammatical Inference for Modeling Mobility Patterns in Networks

2013 ◽  
Vol 12 (11) ◽  
pp. 2119-2131 ◽  
Author(s):  
Sahin Cem Geyik ◽  
Eyuphan Bulut ◽  
Boleslaw K. Szymanski
Keyword(s):  
Grammatical Inference ◽  
Mobility Patterns

scholarly journals Guest editorial to the special issue on grammatical inference

2006 ◽  
Vol 66 (1) ◽  
pp. 3-5 ◽  
Author(s):  
Georgios Paliouras ◽  
Yasubumi Sakakibara
Keyword(s):  
Guest Editorial ◽  
Grammatical Inference ◽  
Special Issue
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