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- ArticleSeptember 2023
Robust Identification in the Limit from Incomplete Positive Data
AbstractIntuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine conditions under which learning algorithms for classes of formal languages are able to succeed when the data presentations are systematically ...
- ArticleJuly 2023
Inference of Over-Constrained NFA of Size to Efficiently and Systematically Derive NFA of Size k for Grammar Learning
- research-articleJanuary 2021
Parallel Algorithms for Minimal Nondeterministic Finite Automata Inference
Fundamenta Informaticae (FUNI), Volume 178, Issue 3Pages 203–227https://doi.org/10.3233/FI-2021-2004The goal of this paper is to develop the parallel algorithms that, on input of a learning sample, identify a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative ...
- articleDecember 2016
Comprehensible predictive models for business processes
Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision ...
- research-articleJanuary 2016
Designing and Learning Substitutable Plane Graph Grammars
- Rémi Eyraud,
- Jean-Christophe Janodet,
- Tim Oates,
- Frédéric Papadopoulos,
- Rémi Eyraud,
- Colin de la Higuera,
- Makoto Kanazawa,
- Ryo Yoshinaka
Fundamenta Informaticae (FUNI), Volume 146, Issue 4Pages 403–430https://doi.org/10.3233/FI-2016-1393Though graph grammars have been widely investigated for 40 years, few learning results exist for them. The main reasons come from complexity issues that are inherent when graphs, and a fortiori graph grammars, are considered. The picture is however ...
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- ArticleMay 2015
Grammatical Inference and Language Frameworks for LANGSEC
SPW '15: Proceedings of the 2015 IEEE Security and Privacy WorkshopsPages 88–98https://doi.org/10.1109/SPW.2015.17Formal Language Theory for Security (LANGSEC) has proposed that formal language theory and grammars be used to define and secure protocols and parsers. The assumption is that by restricting languages to lower levels of the Chomsky hierarchy, it is ...
- articleDecember 2013
Learning trees from strings: a strong learning algorithm for some context-free grammars
Standard models of language learning are concerned with weak learning: the learner, receiving as input only information about the strings in the language, must learn to generalise and to generate the correct, potentially infinite, set of strings ...
- ArticleSeptember 2013
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
ARES '13: Proceedings of the 2013 International Conference on Availability, Reliability and SecurityPages 685–693https://doi.org/10.1109/ARES.2013.90False-positives are a problem in anomaly-based intrusion detection systems. To counter this issue, we discuss anomaly detection for the extensible Markup Language (XML) in a language-theoretic view. We argue that many XML-based attacks target the ...
- articleApril 2013
Query induction with schema-guided pruning strategies
Inference algorithms for tree automata that define node selecting queries in unranked trees rely on tree pruning strategies. These impose additional assumptions on node selection that are needed to compensate for small numbers of annotated examples. ...
- ArticleNovember 2012
Information Extraction from Web Documents Based on Unranked Tree Automaton Inference
MINES '12: Proceedings of the 2012 Fourth International Conference on Multimedia Information Networking and SecurityPages 195–198https://doi.org/10.1109/MINES.2012.128Information extraction (IE) aims at extracting specific information from a collection of documents. A lot of previous work on IE from semi-structured documents (in XML or HTML) uses learning techniques based on strings. Some recent work converts the ...
- ArticleJuly 2012
Random Graph Languages for Distorted and Ambiguous Patterns: Single Layer Model
IMIS '12: Proceedings of the 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous ComputingPages 108–113https://doi.org/10.1109/IMIS.2012.147The work introduces a linguistic based model designed for distorted or ambiguous patterns where a graph based approach is used for structure representation. The knowledge about unevenness is usually created on the basis of finite number of patterns ...
- ArticleDecember 2011
Towards Incremental Learning of Mildly Context-Sensitive Grammars
ICMLA '11: Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 01Pages 223–228https://doi.org/10.1109/ICMLA.2011.146Most models in grammatical inference have been restricted to regular or context-free grammars. As a step towards learning of more powerful grammars, this paper discusses the incremental learning of Linear Indexed Grammars (LIGs) for formal languages ...
- ArticleJune 2011
On dispersed and choice iteration in incrementally learnable dependency types
We study learnability of Categorial Dependency Grammars (CDG), a family of categorial grammars expressing all kinds of projective, discontinuous and repeatable dependencies. For these grammars, it is known that they are not learnable from dependency ...
- ArticleMay 2011
Tarski's principle, categorial grammars and learnability
LATA'11: Proceedings of the 5th international conference on Language and automata theory and applicationsPages 378–389In this paper, functorial languages with the following characteristic are investigated: if two functor-argument structures occur in at last one common functorial context, then they are intersubstitutable on arguments' positions in all elements (...
- ArticleSeptember 2010
Learning fuzzy context-free grammar: a preliminary report
This paper takes up the topic of a task of learning fuzzy context-free grammar from data. The induction process is divided into two phases: first the generic grammar is derived from the positive sentences, next the membership grades are assigned to the ...
- articleAugust 2010
SSGL: a semi-supervised grammar learner
International Journal of Computer Applications in Technology (IJCAT), Volume 38, Issue 4Pages 259–263https://doi.org/10.1504/IJCAT.2010.034526Grammatical inference, also known as Grammar Induction, is about the problem of learning structural models from data. For decades researchers have been trying to devise formal and detailed grammars that would capture the observed regularities of ...
- ArticleJune 2010
Data mining for grammatical inference with bioinformatics criteria
HAIS'10: Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part IIPages 53–60https://doi.org/10.1007/978-3-642-13803-4_7In this paper we describe both theoretical and practical results of a novel data mining process that combines hybrid techniques of association analysis and classical sequentiation algorithms of genomics to generate grammatical structures of a specific ...
- articleApril 2010
Integrating machine learning in intelligent bioinformatics
Machine learning is the adaptive process that makes computers improve from experience, by example, and by analogy. Learning capabilities are essential for automatically enhancing the performance of a computational system over time on the basis of ...
- ArticleFebruary 2010
Machine learning for intelligent bioinformatics: part 1 machine learning integration
The highly-interdisciplinary field of bioinformatics goal is to develop computing systems capable of analyzing molecular biology. We argue that bioinformatics has undergone a historical transition from the first phase to the second, now underway. The ...
- articleJanuary 2010
Clearing Restarting Automata
Restarting automata were introduced as a model for analysis by reduction, which is a linguistically motivated method for checking correctness of a sentence. We propose a new restricted version of restarting automata called clearing restarting automata ...