Abstract
The introductory chapter contains the different formulations of grammatical inference and indicates such formulations that will be considered in this book. Basically, the problem of grammatical inference within the present book is to be studied from machine learning and combinatorial optimization perspectives. In addition to this, the different representations of languages are assumed: deterministic and non-deterministic finite-state automata, regular expressions, and context-free grammars. As regards the machine learning approach, the design and analysis of learning experiments for comparing GI algorithms to each other or with machine learning methods will be discussed. Typical applications of the GI field are presented in this chapter as well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This technique can also be used for algorithms that work on \(S = (S_+, S_-)\), provided that their computational complexity primarily depends on the size of \(S_+\).
- 2.
The median is the number separating the higher half of a data sample, a population, or a probability distribution, from the lower half. If there is an even number of observations, then there is no single middle value; the median is then usually defined to be the mean of the two middle values.
- 3.
Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. It is licensed under a permissive simplified BSD license. Its web page is http://scikit-learn.org/stable/.
References
Alpaydin E (2010) Introduction to machine learning, 2nd edn. The MIT Press
Angluin D (1976) An application of the theory of computational complexity to the study of inductive inference. PhD thesis, University of California
Angluin D (1988) Queries and concept learning. Mach Learn 2(4):319–342
Book RV, Otto F (1993) String-rewriting systems. Springer, Text and Monographs in Computer Science
Bunke H, Sanfelieu A (eds) (1990) Grammatical inference. World Scientific, pp 237–290
Charikar M, Lehman E, Liu D, Panigrahy R, Prabhakaran M, Sahai A, Shelat A (2005) The smallest grammar problem. IEEE Trans Inf Theory 51(7):2554–2576
de la Higuera C (2005) A bibliographical study of grammatical inference. Pattern Recogn 38(9):1332–1348
de la Higuera C (2010) Grammatical inference: learning automata and grammars. Cambridge University Press, New York, NY, USA
Domaratzki M, Kisman D, Shallit J (2002) On the number of distinct languages accepted by finite automata with \(n\) states. J Autom Lang Comb 7:469–486
Dupont P (1994) Regular grammatical inference from positive and negative samples by genetic search: the GIG method. In: Proceedings of 2nd international colloquium on grammatical inference, ICGI ’94, Lecture notes in artificial intelligence, vol 862. Springer, pp 236–245
Eyraud R, de la Higuera C, Janodet J (2007) Lars: a learning algorithm for rewriting systems. Mach Learn 66(1):7–31
Gold EM (1967) Language identification in the limit. Inf Control 10:447–474
Gold EM (1978) Complexity of automaton identification from given data. Inf Control 37:302–320
Grune D, Jacobs CJ (2008) Parsing techniques: a practical guide, 2nd edn. Springer
Heinz J, de la Higuera C, van Zaanen M (2015) Grammatical inference for computational linguistics. Synthesis lectures on human language technologies. Morgan & Claypool Publishers
Hopcroft JE, Motwani R, Ullman JD (2001) Introduction to automata theory, languages, and computation, 2nd edn. Addison-Wesley
Hunt HB III, Rosenkrantz DJ, Szymanski TG (1976) On the equivalence, containment, and covering problems for the regular and context-free languages. J Comput Syst Sci 12:222–268
Imada K, Nakamura K (2009) Learning context free grammars by using SAT solvers. In: Proceedings of the 2009 international conference on machine learning and applications, IEEE computer society, pp 267–272
Japkowicz N, Shah M (2011) Evaluating learning algorithms: a classification perspective. Cambridge University Press
Jiang T, Ravikumar B (1993) Minimal NFA problems are hard. SIAM J Comput 22:1117–1141
Kuroda S (1964) Classes of languages and linear-bounded automata. Inf Control 7(2):207–223
Maurer-Stroh S, Debulpaep M, Kuemmerer N, Lopez de la Paz M, Martins IC, Reumers J, Morris KL, Copland A, Serpell L, Serrano L et al (2010) Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat Methods 7(3):237–242
Meyer AR, Stockmeyer LJ (1972) The equivalence problem for regular expressions with squaring requires exponential space. In: Proceedings of the 13th annual symposium on switching and automata theory, pp 125–129
Moore C, Eppstein D (2003) One-dimensional peg solitaire, and duotaire. In: More games of no chance. Cambridge University Press, pp 341–350
Nowakowski RJ (ed) (1996) Games of no chance. Cambridge University Press
Rozenberg G, Salomaa A (eds) (1997) Handbook of formal languages, vol 3. Beyond words. Springer
Trakhtenbrot B, Barzdin Y (1973) Finite automata: behavior and synthesis. North-Holland Publishing Company
Valiant LG (1984) A theory of the learnable. Commun ACM 27:1134–1142
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Wieczorek, W. (2017). Introduction. In: Grammatical Inference. Studies in Computational Intelligence, vol 673. Springer, Cham. https://doi.org/10.1007/978-3-319-46801-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-46801-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46800-6
Online ISBN: 978-3-319-46801-3
eBook Packages: EngineeringEngineering (R0)