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Volume 57: International Conference on Grammatical Inference, 5-7 October 2016, Delft, The Netherlands
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Editors: Sicco Verwer, Menno van Zaanen, Rick Smetsers
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International Conference on Grammatical Inference 2016: Preface
Sicco Verwer, Menno van Zaanen, Rick Smetsers;
PMLR 57:1-2
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Simple K-star Categorial Dependency Grammars and their Inference
Denis Béchet, Annie Foret;
PMLR 57:3-14
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Query Learning Automata with Helpful Labels
Adrian-Horia Dediu, Joana M. Matos, Claudio Moraga;
PMLR 57:15-29
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Inferring Non-resettable Mealy Machines with $n$ States
Roland Groz, Catherine Oriat, Nicolas Brémond;
PMLR 57:30-41
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Testing Distributional Properties of Context-Free Grammars
Alexander Clark;
PMLR 57:42-53
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Learning Top-Down Tree Transducers with Regular Domain Inspection
Adrien Boiret, Aurélien Lemay, Joachim Niehren;
PMLR 57:54-65
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Using Model Theory for Grammatical Inference: a Case Study from Phonology
Kristina Strother-Garcia, Jerey Heinz, Hyun Jin Hwangbo;
PMLR 57:66-78
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The Generalized Smallest Grammar Problem
Payam Siyari, Matthias Gallé;
PMLR 57:79-92
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Online Grammar Compression for Frequent Pattern Discovery
Shouhei Fukunaga, Yoshimasa Takabatake, Tomohiro I, Hiroshi Sakamoto;
PMLR 57:93-104
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Sp2Learn: A Toolbox for the Spectral Learning of Weighted Automata
Denis Arrivault, Dominique Benielli, François Denis, Remi Eyraud;
PMLR 57:105-119
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Learning Deterministic Finite Automata from Infinite Alphabets
Gaetano Pellegrino, Christian Hammerschmidt, Qin Lin, Sicco Verwer;
PMLR 57:120-131
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Results of the Sequence PredIction ChallengE (SPiCe): a Competition on Learning the Next Symbol in a Sequence
Borja Balle, Rémi Eyraud, Franco M. Luque, Ariadna Quattoni, Sicco Verwer;
PMLR 57:132-136
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Predicting Sequential Data with LSTMs Augmented with Strictly 2-Piecewise Input Vectors
Chihiro Shibata, Jeffrey Heinz;
PMLR 57:137-142
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A Spectral Method that Worked Well in the SPiCe’16 Competition
Farhana Ferdousi Liza, Marek Grześ;
PMLR 57:143-148
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Evaluation of Machine Learning Methods on SPiCe
Ichinari Sato, Kaizaburo Chubachi, Diptarama;
PMLR 57:149-153
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Flexible State-Merging for Learning (P)DFAs in Python
Christian Hammerschmidt, Benjamin Loos, Radu State, Thomas Engel;
PMLR 57:154-159
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Model Selection of Sequence Prediction Algorithms by Compression
Du Xi, Dai Zhuang;
PMLR 57:160-163
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Sequence Prediction Using Neural Network Classiers
Yanpeng Zhao, Shanbo Chu, Yang Zhou, Kewei Tu;
PMLR 57:164-169
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