Bibliography

1: J. Abela, F. Coste, and S. Spina.
Mutually compatible and incompatible merges for the search of the smallest consistent DFA.
In Paliouras and Sakakibara [130], pages 28-39.
2: P. Adriaans, H. Fernau, and M. van Zaanen, editors.
Grammatical Inference: Algorithms and Applications; 6th International Colloquium, ICGI 2002, volume 2484 of LNCS/LNAI. Springer, 2002.
3: P. Adriaans and M. Vervoort.
The EMILE 4.1 grammar induction toolbox.
In Adriaans et al. [2], pages 293-295.
4: H. Ahonen, H. Mannila, and E. Nikunen.
Forming grammars for structured documents: an application of grammatical inference.
In Carrasco and Oncina [26], pages 153-167.
5: R. Alquézar and A. Sanfeliu.
A hybrid connectionist-symbolic approach to regular grammatical inference based on neural learning and hierarchical clustering.
In Carrasco and Oncina [26], pages 203-211.
6: R. Alquézar, A. Sanfeliu, and J. Cueva.
Learning of context-sensitive language acceptors through regular inference and constrained induction.
In Miclet and Higuera [116], pages 134-145.
7: D. Angluin.
Learning and mathematics.
In Paliouras and Sakakibara [130], pages 1-2.
8: M. Antunes and A. L. Oliveira.
Inference of sequential association rules guided by context-free grammars.
In Adriaans et al. [2], pages 1-13.
9: K. Apsitis, R. Freivalds, R. Simanovskis, and J. Smotrovs.
Unions of identifiable families of languages.
In Miclet and Higuera [116], pages 48-58.
10: E. Atwell, S. Arnfied, G. Demetriou, S. Hanlon, J. Hughes, U. Jost, R. Pocock, C. Souter, and J. Ueberla.
Multi-level disambiguation grammar inferred from english corpus, treebank and dictionary.
In Lucas [109], pages 9/1-9/7.
11: P. G. Bagos, T. Liakopoulos, and S. J. Hamodrakas.
Faster gradient descent training of Hidden Markov Models, using individual learning rate adaptation.
In Paliouras and Sakakibara [130], pages 40-52.
12: L. Becerra-Bonache and T. Yokomori.
Learning mild context-sensitiveness: toward understanding children's language learning.
In Paliouras and Sakakibara [130], pages 53-64.
13: D. Béchet, A. Foret, and I. Tellier.
Learnability of pregroup grammars.
In Paliouras and Sakakibara [130], pages 65-76.
14: A. Belz.
PCFG learning by nonterminal partition search.
In Adriaans et al. [2], pages 14-27.
15: P. Bhattacharyya and G. Nagaraja.
Learning a class of regular languages in the probably approximately correct learnability framework of valiant.
In Lucas [109], pages 2/1-2/16.
16: R. Blasig.
Discrete sequence prediction with commented Markov models.
In Miclet and Higuera [116], pages 191-202.
17: A. Brazma, I. Jonassen, J. Vilo, and E. Ukkonen.
Pattern discovery in biosequences.
In Honavar and Slutzki [92], pages 257-270.
18: M. R. Brent and T. A. Cartwright.
Lexical categorization: fitting template grammars by incremental MDL optimization.
In Miclet and Higuera [116], pages 84-94.
19: D. Brooks and M. Lee.
Learning syntax from function words.
In Paliouras and Sakakibara [130], pages 273-274.
20: J. Calera-Rubio and J. Oncina.
Identifying left-right deterministic linear languages.
In Paliouras and Sakakibara [130], pages 283-284.
21: J. Callut and P. Dupont.
A Markovian approach to the induction of regular string distributions.
In Paliouras and Sakakibara [130], pages 77-90.
22: A. Cano, J. Ruiz, and P. García.
Inferring subclasses of regular languages faster using RPNI and forbidden configurations.
In Adriaans et al. [2], pages 28-36.
23: A. Cano, J. Ruiz, and P. García.
Running FCRPNI in efficient time for piecewise and right piecewise testable languages.
In Paliouras and Sakakibara [130], pages 275-276.
24: J. Carme, A. Lemay, and J. Niehren.
Learning node selecting tree transducer from completely annotated examples.
In Paliouras and Sakakibara [130], pages 91-102.
25: R. C. Carrasco, M. L. Forcada, and L. Santamaría.
Inferring stochastic regular grammars with recurrent neural networks.
In Miclet and Higuera [116], pages 274-281.
26: R. C. Carrasco and J. Oncina, editors.
Grammatical Inference and Applications; 2nd International Colloquium, ICGI-94, volume 862 of LNCS/LNAI. Springer, 1994.
27: R. C. Carrasco and J. Oncina.
Learning stochastic regular grammars by means of a state merging method.
In Grammatical Inference and Applications; 2nd International Colloquium, ICGI-94 [26], pages 139-152.
28: R. C. Carrasco, J. Oncina, and J. Calera-Rubio.
Stochastic inference of regular tree languages.
In Honavar and Slutzki [92], pages 187-198.
29: F. Casacuberta.
Statistical estimation of stochastic context-free grammars using the inside-outside algorithm and a transformation on grammars.
In Carrasco and Oncina [26], pages 119-129.
30: F. Casacuberta.
Maximum mutual information and conditional maximum likelihood estimation of stochastic regular syntax-directed translation schemes.
In Miclet and Higuera [116], pages 282-291.
31: F. Casacuberta.
Inference of finite-state transducers by using regular grammars and morphisms.
In Oliveira [125], pages 1-14.
32: F. Casacuberta and C. de la Higuera.
Computational complexity of problems on probabilistic grammars and transducers.
In Oliveira [125], pages 15-24.
33: J. Case, S. Jain, E. Martin, A. Sharma, and F. Stephan.
Identifying clusters from positive data.
In Paliouras and Sakakibara [130], pages 103-114.
34: A. Castellanos.
Approximate learning of random subsequential transducers.
In Honavar and Slutzki [92], pages 67-78.
35: A. Castellanos, I. Galiano, and E. Vidal.
Application of OSTIA to machine translation tasks.
In Carrasco and Oncina [26], pages 93-105.
36: A. Castellanos, E. Vidal, and J. Oncina.
Language understanding and subsequential transducer learning.
In Lucas [109], pages 11/1-11/10.
37: S. K. Chalup and A. D. Blair.
Software for analysing recurrent neural nets that learn to predict non-regular languages.
In Adriaans et al. [2], pages 296-298.
38: J. Chodorowski and L. Miclet.
Applying grammatical inference in learning a language model for oral dialogue.
In Honavar and Slutzki [92], pages 102-113.
39: O. Cicchello and S. C. Kremer.
Beyond EDSM.
In Adriaans et al. [2], pages 37-48.
40: L. Ciortuz.
A framework for inductive learning of typed-unification grammars.
In Adriaans et al. [2], pages 299-302.
41: L.-V. Ciortuz.
Object-oriented inferences in a logical framework for feature grammars.
In Carrasco and Oncina [26], pages 45-56.
42: A. Corbí, J. Oncina, and P. García.
Learning regular languages from a complete sample by error correcting techniques.
In Lucas [109], pages 4/1-4/7.
43: F. Coste and D. Fredouille.
Efficient ambiguity detection in C-NFA, a step towards the inference on non deterministic automata.
In Oliveira [125], pages 25-38.
44: F. Coste, D. Fredouille, C. Kermorvant, and C. de la Higuera.
Introducing domain and typing bias in automata inference.
In Paliouras and Sakakibara [130], pages 115-126.
45: F. Coste and J. Nicolas.
How considering incompatible state mergings may reduce the DFA induction search tree.
In Honavar and Slutzki [92], pages 199-210.
46: P. P. Cruz-Alcázar and E. Vidal.
Learning regular grammars to model musical style: comparing different coding schemes.
In Honavar and Slutzki [92], pages 211-222.
47: G. Dányi.
Regular inference with maximal valid grammar.
In Lucas [109], pages 5/1-5/9.
48: A. Delhay and L. Miclet.
Analogical equations in sequences: definition and resolution.
In Paliouras and Sakakibara [130], pages 127-138.
49: S. Deligne, F. Yvon, and F. Bimbot.
Introducing statistical dependencies and structural constraints in variable-length sequence models.
In Miclet and Higuera [116], pages 156-167.
50: F. Denis, A. Lemay, and A. Terlutte.
Learning regular languages using non deterministic finite automata.
In Oliveira [125], pages 39-50.
51: F. Denis, A. Lemay, and A. Terlutte.
Some classes of regular languages identifiable in the limit from positive data.
In Adriaans et al. [2], pages 63-76.
52: P. Dupont.
Regular grammatical inference from positive and negative samples by genetic search: the GIG method.
In Carrasco and Oncina [26], pages 236-245.
53: P. Dupont.
Incremental regular inference.
In Miclet and Higuera [116], pages 222-237.
54: P. Dupont and J.-C. Amengual.
Smoothing probabilistic automata: an error-correcting approach.
In Oliveira [125], pages 51-64.
55: P. Dupont and L. Chase.
Using symbol clustering to improve probabilistic automaton inference.
In Honavar and Slutzki [92], pages 232-243.
56: P. Dupont, L. Miclet, and E. Vidal.
What is the search space of the regular inference?
In Carrasco and Oncina [26], pages 25-37.
57: J. D. Emerald, K. G. Subramanian, and D. G. Thomas.
Learning code regular and code linear languages.
In Miclet and Higuera [116], pages 211-221.
58: J. D. Emerald, K. G. Subramanian, and D. G. Thomas.
Learning a subclass of context-free languages.
In Honavar and Slutzki [92], pages 223-231.
59: J. D. Emerald, K. G. Subramanian, and D. G. Thomas.
Inferring subclasses of contextual languages.
In Oliveira [125], pages 65-74.
60: Y. Esposito, A. Lemay, F. Denis, and P. Dupont.
Learning probabilistic residual finite state automata.
In Adriaans et al. [2], pages 77-91.
61: R. Eyraud, C. de la Higuera, and J.-C. Janodet.
Representing languages by learnable rewriting systems.
In Paliouras and Sakakibara [130], pages 139-150.
62: J. A. Feldman.
Real language learning.
In Honavar and Slutzki [92], pages 114-125.
63: H. Fernau.
Fragmentation: enhancing identifiability.
In Adriaans et al. [2], pages 92-105.
64: H. Fernau.
Extracting minimum length document type definitions is NP-hard.
In Paliouras and Sakakibara [130], pages 277-278.
65: H. Fernau and J. M. Sempere.
Permutations and control sets for learning non-regular language families.
In Oliveira [125], pages 75-88.
66: L. Firoiu, T. Oates, and P. R. Cohen.
Learning deterministic finite automaton with a recurrent neural network.
In Honavar and Slutzki [92], pages 90-101.
67: N. Flann.
Integrating segmentation and recognition in on-line cursive handwriting using error-correcting grammars.
In Lucas [109], pages 23/1-23/6.
68: P. Fletcher.
Neural networks for learning grammars.
In Lucas [109], pages 15/1-15/8.
69: C. Costa Florêncio.
On the complexity of consistent identification of some classes of structure languages.
In Oliveira [125], pages 89-102.
70: C. Costa Florêncio.
Consistent identification in the limit of rigid grammars from strings is NP-hard.
In Adriaans et al. [2], pages 49-62.
71: A. Foret and Y. Le Nir.
On limit points for some variants of rigid Lambek grammars.
In Adriaans et al. [2], pages 106-119.
72: A. L. N. Fred.
Clustering of sequences using minimum grammar compexity criterion.
In Miclet and Higuera [116], pages 107-116.
73: A. L. N. Fred.
Computation of substring probabilities in stochastic grammars.
In Oliveira [125], pages 103-114.
74: I. Galiano and E. Segarra.
The application of k-testable languages in the strict sense to phone recognition in automatic speech recognition.
In Lucas [109], pages 22/1-22/7.
75: P. Gamallo, G. P. Lopes, and J. F. Da Silva.
A divide-and-conquer approach to acquire syntactic categories.
In Paliouras and Sakakibara [130], pages 151-162.
76: P. García, A. Cano, and J. Ruiz.
A comparative study of two algorithms for automata identification.
In Oliveira [125], pages 115-126.
77: J. Geertzen and M. van Zaanen.
Grammatical inference using suffix trees.
In Paliouras and Sakakibara [130], pages 163-174.
78: J.-Y. Giordano.
Version space for learning context-free grammars.
In Lucas [109], pages 3/1-3/8.
79: J.-Y. Giordano.
Inference of context-free grammars by enumeration: structural containment as an ordering bias.
In Carrasco and Oncina [26], pages 212-221.
80: J.-Y. Giordano.
Grammatical inference using tabu search.
In Miclet and Higuera [116], pages 292-300.
81: M. Golea, M. Matsuoka, and Y. Sakakibara.
Stochastic simple recurrent neural networks.
In Miclet and Higuera [116], pages 262-273.
82: J. Gregor and M. G. Thomason.
A disagreement count scheme for inference of constrained Markov networks.
In Miclet and Higuera [116], pages 168-178.
83: G. Guimarães.
The induction of temporal grammatical rules from multivariate time series.
In Oliveira [125], pages 127-140.
84: A. Habrard, M. Bernard, and F. Jacquenet.
Generalized stochastic tree automata for multi-relational data mining.
In Adriaans et al. [2], pages 120-133.
85: C. de la Higuera.
Characteristic sets for polynomial grammatical inference.
In Miclet and Higuera [116], pages 59-71.
86: C. de la Higuera.
Learning stochastic finite automata from experts.
In Honavar and Slutzki [92], pages 79-89.
87: C. de la Higuera and J. Oncina.
On sufficient conditions to identify classes of grammars from polynomial time and data.
In Adriaans et al. [2], pages 134-148.
88: C. de la Higuera and J. Oncina.
Learning stochastic finite automata.
In Paliouras and Sakakibara [130], pages 175-186.
89: C. de la Higuera, J. Oncina, and E. Vidal.
Identification of DFA: data-dependent vs data-independent algorithms.
In Miclet and Higuera [116], pages 313-325.
90: C. de la Higuera and F. Thollard.
Identification in the limit with probability one of stochastic deterministic finite automata.
In Oliveira [125], pages 141-156.
91: J. Hollatz.
Rule based knowledge in neural computing.
In Lucas [109], pages 19/1-19/8.
92: V. Honavar and G. Slutzki, editors.
Grammatical Inference; 4th International Colloquium, ICGI-98, volume 1433 of LNCS/LNAI. Springer, 1998.
93: A. Itai.
Learning morphology--practice makes good.
In Carrasco and Oncina [26], pages 5-15.
94: M. Jardino and G. Adda.
Automatic determination of a stochastic bi-gram class language model.
In Carrasco and Oncina [26], pages 57-65.
95: E. Jeltsch and H.-J. Kreowski.
Grammatical inference based on hyperedge replacement.
In Lucas [109], pages 7/1-7/6.
96: G. J. F. Jones, H. Lloyd-Thomas, and J. H. Wright.
Adaptive statistical and grammar models of language for application to speech recognition.
In Lucas [109], pages 25/1-25/8.
97: H. Juillé and J. B. Pollack.
A stochastic search approach to grammar induction.
In Honavar and Slutzki [92], pages 126-137.
98: N. Karampatziakis, G. Paliouras, D. Pierrakos, and P. Stamatopoulos.
Navigation pattern discovery using grammatical inference.
In Paliouras and Sakakibara [130], pages 187-198.
99: C. Kermorvant and P. Dupont.
Stochastic grammatical inference with multinomial tests.
In Adriaans et al. [2], pages 149-160.
100: C. Kermorvant and C. de la Higuera.
Learning languages with help.
In Adriaans et al. [2], pages 161-173.
101: V. Keselj.
Grammar model and grammar induction in the system NL PAGE.
In Honavar and Slutzki [92], pages 57-66.
102: T. Knuutila.
Inductive inference from positive data: from heuristic to characterizing methods.
In Miclet and Higuera [116], pages 22-47.
103: S. Kobayashi.
Iterated transductions and efficient learning from positive data: a unifying view.
In Oliveira [125], pages 157-170.
104: H.-U. Krieger.
A corpus-driven context-free approximation of head-driven phrase structure grammar.
In Paliouras and Sakakibara [130], pages 199-210.
105: K. J. Lang, B. A. Pearlmutter, and R. A. Price.
Results of the Abbadingo One DFA learning competition and a new evidence-driven state merging algorithm.
In Honavar and Slutzki [92], pages 1-12.
106: J. A. Laxminarayana, J. M. Sempere, and G. Nagaraja.
Learning distinguishable linear grammars from positive data.
In Paliouras and Sakakibara [130], pages 279-280.
107: S. Lievesley and E. Atwell.
'NAIL': artificial intelligence software for learning natural language.
In Adriaans et al. [2], pages 306-308.
108: D. Lorenzo.
Inductive logic programming for discrete event systems.
In Miclet and Higuera [116], pages 250-261.
109: S. Lucas, editor.
Grammatical Inference: Theory, Applications and Alternatives; 1st International Colloquium. The Institution of Electrical Engineers, 1993.
110: S. Lucas.
New directions in grammatical inference.
In Grammatical Inference: Theory, Applications and Alternatives; 1st International Colloquium [109], pages 1/1-1/7.
111: S. M. Lucas, E. Vidal, A. Amiri, S. Hanlon, and J.-C. Amengual.
A comparison of syntactic and statistical techniques for off-line OCR.
In Carrasco and Oncina [26], pages 168-179.
112: G. Sampson M. D. Dennis, A. M. Wallington.
Stochastic optimization of a probabilistic language model.
In Carrasco and Oncina [26], pages 271-281.
113: D. M. Magerman.
Learning grammatical stucture using statistical decision-trees.
In Miclet and Higuera [116], pages 1-21.
114: C. de Marcken.
The acquisition of a lexicon from paired phoneme sequences and semantic representations.
In Carrasco and Oncina [26], pages 66-77.
115: P. Martinek.
An inverse limit of context-free grammars--a new approach to identifiability in the limit.
In Oliveira [125], pages 171-185.
116: L. Miclet and C. de la Higuera, editors.
Grammatical Inference: Learning Syntax from Sentences; 3rd International Colloquium, ICGI-96, volume 1147 of LNCS/LNAI. Springer, 1996.
117: E. Moreau.
Partial learning using link grammars data.
In Paliouras and Sakakibara [130], pages 211-222.
118: K. Nakamura.
Extending incremental learning of context free grammars in synapse.
In Paliouras and Sakakibara [130], pages 281-282.
119: K. Nakamura and T. Ishiwata.
Synthesizing context free grammars from sample strings based on inductive CYK algorithm.
In Oliveira [125], pages 186-195.
120: K. Nakamura and M. Matsumoto.
Incremental learning of context free grammars.
In Adriaans et al. [2], pages 174-184.
121: S. Neumann.
Grammatical inference in dacs.
In Lucas [109], pages 10/1-10/6.
122: F. Nevado, J.-A. Sánchez J., and J.-M. Benedí.
Combination of estimation algorithms and grammatical inference techniques to learn stochastic context-free grammars.
In Oliveira [125], pages 196-206.
123: M. A. Casta no, E. Vidal, and F. Casacuberta.
Inference of stochastic regular languages through simple recurrent networks.
In Lucas [109], pages 16/1-16/6.
124: T. Oates and B. Heeringa.
Estimating grammar parameters using bounded memory.
In Adriaans et al. [2], pages 185-198.
125: A. L. Oliveira, editor.
Grammatical Inference: Algorithms and Applications; 5th International Colloquium, ICGI 2000, volume 1891 of LNCS/LNAI. Springer, 2000.
126: J. Oncina.
The data driven approach applied to the OSTIA algorithm.
In Honavar and Slutzki [92], pages 50-56.
127: J. Oncina and M. A. Varó.
Using domain information during the learning of a subsequential transducer.
In Miclet and Higuera [116], pages 301-312.
128: M. Osborne and D. Bridge.
Inductive and deductive learning of grammar: dealing with incomplete theories.
In Lucas [109], pages 13/1-13/10.
129: M. Osborne and D. G. Bridge.
Learning unification-based grammars using the spoken english corpus.
In Carrasco and Oncina [26], pages 260-270.
130: G. Paliouras and Y. Sakakibara, editors.
Grammatical Inference: Algorithms and Applications; 7th International Colloquium, ICGI 2004, volume 3264 of LNCS/LNAI. Springer, 2004.
131: R. Parekh and V. Honavar.
An incremental interactive algorithm for grammar inference.
In Miclet and Higuera [116], pages 238-249.
132: R. Parekh and V. Honavar.
On the relationship between models for learning in helpful environments.
In Oliveira [125], pages 207-220.
133: R. Parekh, C. Nichitiu, and V. Honavar.
A polynominal time incremental algorithm for learning DFA.
In Honavar and Slutzki [92], pages 37-49.
134: G. Petasis, G. Paliouras, C. D. Spyropoulos, and C. Halatsis.
eg-GRIDS: context-free grammatical inference from positive examples using genetic search.
In Paliouras and Sakakibara [130], pages 223-234.
135: D. Picó and E. Vidal.
Transducer-learning experiments on language understanding.
In Honavar and Slutzki [92], pages 138-149.
136: S. della Pietra, V. J. Della Pietra, J. Gillet, J. D. Lafferty, H. Printz, and L. Ures.
Inference and estimation of a long-range trigram model.
In Carrasco and Oncina [26], pages 78-92.
137: J. R. Rico-Juan, J. Calera-Rubio, and R. C. Carrasco.
Probabilistic -testable tree languages.
In Oliveira [125], pages 221-228.
138: J. R. Rico-Juan, J. Calera-Rubio, and R. C. Carrasco.
Stochastic -testable tree languages and applications.
In Adriaans et al. [2], pages 199-212.
139: M. Roques.
Dynamic grammatical representations in guided propagation networks.
In Carrasco and Oncina [26], pages 189-202.
140: P. Rossmanith and T. Zeugmann.
Learning -variable pattern languages efficiently stochastically finite on average from positive data.
In Honavar and Slutzki [92], pages 13-24.
141: J. Ruiz, S. Espa�a, and P. García.
Locally threshold testable languages in strict sense: application to the inference problem.
In Honavar and Slutzki [92], pages 150-161.
142: J. Ruiz and P. Garcia.
The algorithms RT and -TTI: a first comparison.
In Carrasco and Oncina [26], pages 180-188.
143: J. Ruiz and P. García.
Learning -piecewise testable languages from positive data.
In Miclet and Higuera [116], pages 203-210.
144: A. S. Saidi and S. Tayeb-bey.
Grammatical inference in document recognition.
In Honavar and Slutzki [92], pages 175-186.
145: Y. Sakakibara and H. Muramatsu.
Learning context-free grammars from partially structured examples.
In Oliveira [125], pages 229-240.
146: H. Sakamoto, H. Arimura, and S. Arikawa.
Identification of tree translation rules from examples.
In Oliveira [125], pages 241-255.
147: C. Samuelsson, P. Tapanainen, and A. Voutilainen.
Inducing constraint grammars.
In Miclet and Higuera [116], pages 146-155.
148: J. A. Sánchez and J.-M. Benedí.
Statistical inductive learning of regular formal languages.
In Carrasco and Oncina [26], pages 130-138.
149: Y. Seginer.
Fast learning from strings of 2-letter rigid grammars.
In Adriaans et al. [2], pages 213-224.
150: S. Seki and S. Kobayashi.
Efficient learning of -reversible context-free grammars from positive structural examples.
In Paliouras and Sakakibara [130], pages 285-287.
151: J. M. Sempere and A. Fos.
Learning linear grammars from structural information.
In Miclet and Higuera [116], pages 126-133.
152: J. M. Sempere and P. García.
A new regular language learning algorithm from lexicographically ordered complete samples.
In Lucas [109], pages 6/1-6/7.
153: J. M. Sempere and P. García.
A characterization of even linear languages and its application to the learning problem.
In Carrasco and Oncina [26], pages 38-44.
154: J. M. Sempere and P. García.
Learning locally testable even linear languages from positive data.
In Adriaans et al. [2], pages 225-236.
155: J. M. Sempere and G. Nagaraja.
Learning a subclass of linear languages from positive structural information.
In Honavar and Slutzki [92], pages 162-174.
156: A. J. C. Sharkey and N. E. Sharkey.
Connectionism and natural language.
In Lucas [109], pages 20/1-20/10.
157: R. A. Sharman.
Probabilistic dependancy grammar, and its application in constructing language models for speech application.
In Lucas [109], pages 12/1-12/11.
158: D. Dudau Sofronie, I. Tellier, and M. Tommasi.
A tool for language learning based on categorial grammars and semantic information.
In Adriaans et al. [2], pages 303-305.
159: B. Starkie.
Inferring attribute grammars with structured data for natural language processing.
In Adriaans et al. [2], pages 237-248.
160: B. Starkie, F. Coste, and M. van Zaanen.
The omphalos context-free grammar learning competition.
In Paliouras and Sakakibara [130], pages 16-27.
161: B. Starkie and H. Fernau.
The Boisdale algorithm - an induction method for a subclass of unification grammar from positive data.
In Paliouras and Sakakibara [130], pages 235-247.
162: B. Starkie, G. Findlow, K. Ho, A. Hui, L. Law, L. Lightwood, S. Michnowicz, and C. Walder.
Lyrebird $^{TM}$ : developing spoken dialog systems using examples.
In Adriaans et al. [2], pages 309-311.
163: F. Stephan and S. A. Terwijn.
Counting extensional differences in BC-learning.
In Oliveira [125], pages 255-269.
164: A. Stolcke and S. M. Omohundro.
Inducing probabilistic grammars by Bayesian model merging.
In Carrasco and Oncina [26], pages 106-118.
165: N. Sugimoto, T. Toyoshima, S. Shimozono, and K. Hirata.
Constructive learning of context-free languages with a subpansive tree.
In Oliveira [125], pages 270-283.
166: Y. Tajima, Y. Kotani, and M. Terada.
An analysis of examples and a search space for PAC learning of simple deterministic languages with membership queries.
In Paliouras and Sakakibara [130], pages 288-289.
167: Y. Tajima and M. Terada.
A PAC learnability of simple deterministic languages.
In Adriaans et al. [2], pages 249-260.
168: Y. Tajima and E. Tomita.
A polynomial time learning algorithm of simple deterministic languages via membership queries and a representative sample.
In Oliveira [125], pages 284-297.
169: Y. Takada.
A hierarchy of language families learnable by regular language learners.
In Carrasco and Oncina [26], pages 16-24.
170: Y. Takada and T. Y. Nishida.
A note on grammatical inference of slender context-free languages.
In Miclet and Higuera [116], pages 117-125.
171: I. Tellier.
Meaning helps learning syntax.
In Honavar and Slutzki [92], pages 25-36.
172: S. A. Terwijn.
On the learnability of Hidden Markov Models.
In Adriaans et al. [2], pages 261-268.
173: F. Thollard and A. Clark.
Shallow parsing using probabilistic grammatical inference.
In Adriaans et al. [2], pages 269-282.
174: F. Thollard and A. Clark.
Learning stochastic deterministic regular languages.
In Paliouras and Sakakibara [130], pages 248-259.
175: D. G. Thomas, M. Humrosia Begam, K. G. Subramanian, and S. Gnanasekaran.
Learning of regular bi- $\omega$ languages.
In Adriaans et al. [2], pages 283-292.
176: C. Tillmann and H. Ney.
Selection criteria for word trigger pairs in language modelling.
In Miclet and Higuera [116], pages 95-106.
177: E. Vidal.
Grammatical inference: an introduction survey.
In Carrasco and Oncina [26], pages 1-4.
178: E. Vidal and F. Casacuberta.
Learning finite-state models for machine translation.
In Paliouras and Sakakibara [130], pages 3-15.
179: E. Vidal and D. Llorens.
Using knowledge to improve -gram language modelling through the MGGI methodology.
In Miclet and Higuera [116], pages 179-190.
180: E. Vidal, H. Rulot, J. M. Valiente, and G. Andreu.
Application of the error-correcting grammatical inference algorithm (ecgi) to planar shape recognition.
In Lucas [109], pages 24/1-24/10.
181: P. Viechnicki.
A performance evaluation of automatic survey classifiers.
In Honavar and Slutzki [92], pages 244-256.
182: J. M. Vilar.
Query learning of subsequential transducers.
In Miclet and Higuera [116], pages 72-83.
183: J. M. Vilar.
Improve the learning of subsequential transducers by using alignments and dictionaries.
In Oliveira [125], pages 298-311.
184: H.-M. Voigt, J. Born, and I. Santibanez-Koref.
Design of artificial neural networks with stochastic graph-l-system.
In Lucas [109], pages 18/1-18/8.
185: M. Wakatsuki, K. Teraguchi, and E. Tomita.
Polynomial time identification of strict deterministic restricted one-counter automata in some class from positive data.
In Paliouras and Sakakibara [130], pages 260-272.
186: J. H. Wright, G. J. F. Jones, and H. Lloyd-Thomas.
Training and application of integrated grammar/bigram language models.
In Carrasco and Oncina [26], pages 246-259.
187: A. Wrigley.
Parse tree n-grams for spoken language modelling.
In Lucas [109], pages 26/1-26/6.
188: P. J. Wyard.
Representational issues for context free grammar induction using genetic algorithms.
In Carrasco and Oncina [26], pages 222-235.
189: S. J. Young and H.-H. Shih.
Computer assisted grammar construction.
In Carrasco and Oncina [26], pages 282-290.
190: M. van Zaanen.
Implementing alignment-based learning.
In Adriaans et al. [2], pages 312-314.

Thu Jul 7 23:21:51 CEST 2005