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

References and models cited by this paper

References and models that cite this paper

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programming to close the loop in machine learning Proceedings of the AISB99 symposium on AI and scientific creativity (unpublished manuscript)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

Everitt BS, Landau S, Leese M (2001) Cluster analysis

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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