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Jacobsson H (2006) The crystallizing substochastic sequential machine extractor: CrySSMEx. Neural Comput 18:2211-55 [PubMed]

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Andrews R, Diederich J, Tickle AB (1995) Survey and critique of techniques for extracting rules from trained artificial neural networks Knowledge Based Systems 8:373-389
Angluin D (1987) Learning regular sets from queries and counterexamples Information And Computation 75:87-106
Angluin D (2004) Queries revisited Theoretical Computer Science 313:175-194
Bergadano F, Gunetti D (1996) Testing by means of inductive program learning ACM Transactions On Software Engineering And Methodology 5:119-145
Blair A, Pollack J (1997) Analysis of dynamical recognizers Neural Comput 9:1127-1142
Boden M, Wiles J (2000) Context-free and context-sensitive dynamics in recurrent neural networks Connection Science 12:196-210
Bryant CH, Muggleton SH, Page CD, Sternberg MJE (1999) Combining active learning with inductive logic programming to close the loop in machine learning Proceedings of the AISB99 symposium on AI and scientific creativity (unpublished manuscript)
Casey M (1996) The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction. Neural Comput 8:1135-78 [PubMed]
Chaitin GJ (1987) Algorithmic information theory
Christiansen MH, Chater N (1999) Toward a connectionist model of recursion in human linguistic performance Cogn Scien 23:157-205
Cleeremans A, Servan-Schreiber D, Mcclelland JL (1989) Finite state automata and simple recurrent networks Neural Comput 1:372-381
Cohn DA, Atlas L, Ladner RE (1994) Improving generalization with active learning Mach Learn 15:201-221
Colton S, Bundy A, Walsh T (2000) On the notion of interestingness in automated mathematical discovery Int J Human Computer Stud 53:351-375
Cover TM, Thomas JA (1991) Elements of Information Theory
Craven MW, Shavlik JW (1994) Using sampling and queries to extract rules from trained neural networks Machine learning: Proceedings of the Eleventh International Conference, Cohen WW:Hirsh H, ed.
Craven MW, Shavlik JW (1996) Extracting tree-structured representations of trained networks Advances in neural information processing systems, Touretzky D:Mozer M:Hasselmo M, ed. pp.24
Craven MW, Shavlik JW (1999) Rule extraction: Where do we go from here? Tech. Rep. No. Machine Learning Research Group Working Paper 99-1
Crutchfield JP (1994) The calculi of emergence: Computation, dynamics, and induction Physica D 75:11-54
Crutchfield JP, Young K (1990) Computation at the onset of chaos Complexity, entropy and the physics of information, Zurek W, ed.
de_la_Higuera C (2005) A bibliographical study of grammatical inference Pattern Recognition 38:1332-1348
Devaney RL (1992) A First Course In Chaotic Dynamical Systems: Theory And Experiment
Elman JL (1990) Finding structure in time Cognitive Science 14:179-211
Gers FA, Schmidhuber E (2001) LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans Neural Netw 12:1333-40 [Journal] [PubMed]
Giles CL, Miller CB, Chen D, Chen HH, Sun GZ, Lee YC (1992) Learning and extracting finite state automata with second-order recurrent neural networks Neural Comput 4:393-405
Giles CL, Miller CB, Chen D, Sun GZ, Chen HH, Lee YC (1992) Extracting and learning an unknown grammar with recurrent neural networks Advances in neural information processing systems, Moody JE:Hanson SJ:Lippman RP, ed. pp.317
Gold ME (1967) Language identification in the limit Information And Control 10:447-474
Hammer B, Tino P (2003) Recurrent neural networks with small weights implement definite memory machines Neural Comput 15:1897-1929
Hopcroft J, Ullman J (1979) Introduction to automata theory, languages, and computation
Jacobsson H (2005) Rule extraction from recurrent neural networks: A taxonomy and review Neural Comput 17:1223-1263
Jacobsson H, Ziemke T (2003) Improving procedures for evaluation of connectionist context-free language predictors. IEEE Trans Neural Netw 14:963-6 [Journal] [PubMed]
Jacobsson H, Ziemke T (2003) Reducing complexity of rule extraction from prediction RNNs through domain interaction Tech. Rep. No. HS-IDA-TR-03-007
Jacobsson H, Ziemke T (2005) Rethinking rule extraction from recurrent neural networks Paper presented at the IJCAI-05 Workshop on Neural-Symbolic Learning and Reasoning
Kolen JF, Kremer SC (2001) A field guide to dynamical recurrent networks, Kolen JF:Kremer SC, ed.
Kremer SC (2001) Spatiotemporal connectionist networks: A taxonomy and review Neural Comput 13:248-306
Kumar R, Garg VK (2001) Control of stochastic discrete event systems modeled by probabilistic languages IEEE Transactions On Automatic Control 46:593-606
Lang KJ (1992) Random DFAs can be approximately learned from sparse uniform examples Proceedings of the Fifth ACM Workshop on Computational Learning Theory :45-52
Langley P, Shrager J, Saito K (2002) Computational discovery of communicable scientific knowledge Logical and computational aspects of model-based reasoning, Magnani L:Nersessian NJ:Pizzi C, ed.
Ljung L (1999) System identification: Theory for the user (2nd ed)
Manolios P, Fanelli R (1994) First order recurrent neural networks and deterministic finite state automata Neural Comput 6:1155-1173
Marculescu D, Marculescu R, Pedram M (1996) Stochastic sequential machine synthesis targeting constrained sequence generation Dac96: Proceedings of the 33rd Annual Conference on Design Automation :696-701
Mcculloch WS, Pitts W (1943) A Logical Calculus of Ideas Immanent in Nervous Activity Bull Math Biophysics 5:115-133
Moore EF (1956) Gedanken-experiments on sequential machines Annals Of Mathematical Studies, Shannon CE:McCarthy J, ed. pp.129
Muggleton S, Raedt LD (1994) Inductive logic programming: Theory and methods Journal of Logic Programming 19:629-679
Paz A (1971) Introduction to probabilistic automata
Popper KR (1990) The logic of scientific discovery (14th ed)
Rabin MO (1963) Probabilistic automata Information And Control 6:230-245
Sharkey NE, Jackson SA (1995) An internal report for connectionists Computational architectures integrating neural and symbolic processes , Sun R:Bookman LA, ed. pp.223
Simon HA (1973) Does scientific discovery have a logic? Philosophy Of Science 40:471-480
Simon HA (1996) Machine discovery Foundations Of Science 1:171-200
Tickle AB, Andrews R, Golea M, Diederich J (1998) The truth will come to light: Directions and challenges in extracting the knowledge embedded within mined artificial neural networks IEEE Transactions On Neural Networks 9:1057-1068
Tino P, Cernanský M, Benusková L (2004) Markovian architectural bias of recurrent neural networks. IEEE Trans Neural Netw 15:6-15 [Journal] [PubMed]
Tino P, Köteles M (1999) Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences. IEEE Trans Neural Netw 10:284-302 [Journal] [PubMed]
Tino P, Vojtek V (1998) Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences Neural Network World 8:517-530
Tonkes B, Blair A, Wiles J (1998) Inductive bias in context-free language learning Proceedings of the Ninth Australian Conference on Neural Networks :52-56
Vahed A, Omlin CW (2004) A machine learning method for extracting symbolic knowledge from recurrent neural networks. Neural Comput 16:59-71 [PubMed]
Valiant LG (1984) A theory of the learnable Communications Of The ACM 27:1134-1142
Watrous RL, Kuhn GM (1992) Induction of finite-state automata using second-order recurrent networks Advances in neural information processing systems, Moody JE:Hanson SJ:Lippman RP, ed. pp.309
Wiles J, Elman JL (1995) Learning to count without a counter: A case study of dynamics and activation landscapes in recurrent neural networks Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society :482-487
Williamson J (2004) A dynamic interaction between machine learning and the philosophy of science Minds And Machines 14:539-549
Young S, Garg VK (1995) Model uncertainty in discrete event systems SIAM Journal On Control And Optimization 33:208-226
Zeng Z, Goodman RM, Smyth P (1993) Learning finite state machines with self-clustering recurrent networks Neural Comput 5:976-990
Grüning A (2007) Elman backpropagation as reinforcement for simple recurrent networks. Neural Comput 19:3108-31 [Journal] [PubMed]
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