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Enrique Vidal, Frank Thollard, Colin de la Higuera, Francisco Casacuberta, Rafael C. Carrasco, "Probabilistic FiniteState MachinesPart II," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 7, pp. 10261039, July, 2005.  
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@article{ 10.1109/TPAMI.2005.148, author = {Enrique Vidal and Frank Thollard and Colin de la Higuera and Francisco Casacuberta and Rafael C. Carrasco}, title = {Probabilistic FiniteState MachinesPart II}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {27}, number = {7}, issn = {01628828}, year = {2005}, pages = {10261039}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.148}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Probabilistic FiniteState MachinesPart II IS  7 SN  01628828 SP1026 EP1039 EPD  10261039 A1  Enrique Vidal, A1  Frank Thollard, A1  Colin de la Higuera, A1  Francisco Casacuberta, A1  Rafael C. Carrasco, PY  2005 KW  Index Terms Automata KW  classes defined by grammars or automata KW  machine learning KW  language acquisition KW  language models KW  language parsing and understanding KW  machine translation KW  speech recognition and synthesis KW  structural pattern recognition KW  syntactic pattern recognition. VL  27 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
[1] E. Vidal, F. Thollard, C. de la Higuera, F. Casacuberta, and R.C. Carrasco, “Probabilistic FiniteState Automata— Part I,” IEEE Trans. Pattern Analysis and Machine Intelligence, special issue on syntactic and structural pattern recognition, vol 27, no. 7, pp. 10131025, July 2005.
[2] L.E. Baum, “An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Processes,” Inequalities, vol. 3, pp. 18, 1972.
[3] C.F.J. Wu, “On the Convergence Properties of the EM Algorithm,” Annals of Statistics, vol. 11, no. 1, pp. 95103, 1983.
[4] F. Casacuberta, “Statistical Estimation of Stochastic ContextFree Grammars,” Pattern Recognition Letters, vol. 16, pp. 565573, 1995.
[5] F. Casacuberta, “Growth Transformations for Probabilistic Functions of Stochastic Grammars,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 10, no. 3, pp. 183201, 1996.
[6] G.J. McLachlan and T. Krishnan, The EM Algorithm and Extensions. Wiley, 1997.
[7] D. Picó and F. Casacuberta, “Some StatisticalEstimation Methods for Stochastic FiniteState Transducers,” Machine Learning J., vol. 44, no. 1, pp. 121141, 2001.
[8] P. Dupont, F. Denis, and Y. Esposito, “Links between Probabilistic Automata and Hidden Markov Models: Probability Distributions, Learning Models and Induction Algorithms,” Pattern Recognition, 2004.
[9] I.H. Witten and T.C. Bell, “The Zero Frequency Problem: Estimating the Probabilities of Novel Events in Adaptive Test Compression,” IEEE Trans. Information Theory, vol. 37, no. 4, pp. 10851094, 1991.
[10] H. Ney, S. Martin, and F. Wessel, CorpusBased Statiscal Methods in Speech and Language Processing, S. Young and G. Bloothooft, eds., pp. 174207, Kluwer Academic Publishers, 1997.
[11] P. Dupont and J.C. Amengual, “Smoothing Probabilistic Automata: An ErrorCorrecting Approach,” Proc. Fifth Int'l Colloquium Grammatical Inference: Algorithms and Applications, pp. 5156, 2000.
[12] Y. Sakakibara, M. Brown, R. Hughley, I. Mian, K. Sjolander, R. Underwood, and D. Haussler, “Stochastic ContextFree Grammars for tRNA Modeling,” Nuclear Acids Research, vol. 22, pp. 51125120, 1994.
[13] T. Kammeyer and R.K. Belew, “Stochastic ContextFree Grammar Induction with a Genetic Algorithm Using Local Search,” Foundations of Genetic Algorithms IV, R.K. Belew and M. Vose, eds., 1996.
[14] N. Abe and H. Mamitsuka, “Predicting Protein Secondary Structure Using Stochastic Tree Grammars,” Machine Learning J., vol. 29, pp. 275301, 1997.
[15] R.C. Carrasco, J. Oncina, and J. CaleraRubio, “Stochastic Inference of Regular Tree Languages,” Machine Learning J., vol. 44, no. 1, pp. 185197, 2001.
[16] M. Kearns and L. Valiant, “Cryptographic Limitations on Learning Boolean Formulae and Finite Automata,” Proc. 21st ACM Symp. Theory of Computing, pp. 433444, 1989.
[17] N. Abe and M. Warmuth, “On the Computational Complexity of Approximating Distributions by Probabilistic Automata,” Machine Learning J., vol. 9, pp. 205260, 1992.
[18] M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, R.E. Schapire, and L. Sellie, “On the Learnability of Discrete Distributions,” Proc. 25th Ann. ACM Symp. Theory of Computing, pp. 273282, 1994.
[19] D. Ron, Y. Singer, and N. Tishby, “On the Learnability and Usage of Acyclic Probabilistic Finite Automata,” Proc. Conf. Learning Theory, pp. 3140, 1995.
[20] A. Stolcke and S. Omohundro, “Inducing Probabilistic Grammars by Bayesian Model Merging,” Proc. Second Int'l Colloquium Grammatical Inference and Applications, pp. 106118, 1994.
[21] F. Jelinek, Statistical Methods for Speech Recognition. Cambridge, Mass.: MIT Press, 1998.
[22] P. García and E. Vidal, “Inference of kTestable Languages in the Strict Sense and Application to Syntactic Pattern Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 9, pp. 920925, Sept. 1990.
[23] Y. Zalcstein, “Locally Testable Languages,” J. Computer and System Sciences, vol. 6, pp. 151167, 1972.
[24] R. McNaughton, “Algebraic Decision Procedures for Local Testability,” Math. System Theory, vol. 8, no. 1, pp. 6067, 1974.
[25] E. Vidal and D. Llorens, “Using Knowledge to Improve NGram Language Modelling through the MGGI Methodology,” Proc. Third Int'l Colloquium Grammatical Inference: Learning Syntax from Sentences, pp. 179190, 1996.
[26] S. Eilenberg, Automata, Languages and Machines. Vol. A. New York: Academic, 1974.
[27] L. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recoginition,” Proc. IEEE, vol. 77, pp. 257286, 1989.
[28] J. Picone, “Continuous Speech Recognition Using Hidden Markov Models,” IEEE ASSP Magazine, vol. 7, no. 3, pp. 2641, 1990.
[29] I. Bazzi, R. Schwartz, and J. Makhoul, “An Omnifont OpenVocabulary OCR System for English and Arabic,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 6, pp. 495504, June 1999.
[30] A. Toselli, A. Juan, D. Keysers, J. González, I. Salvador, H. Ney, E. Vidal, and F. Casacuberta, “Integrated Handwriting Recognition and Interpretation Using Finite State Models,” Int'l J. Pattern Recognition and Artificial Intelligence, 2004.
[31] F. Casacuberta, “FiniteState Transducers for SpeechInput Translation,” Proc. Workshop Automatic Speech Recognition and Understanding, Dec. 2001.
[32] F. Casacuberta, E. Vidal, and J.M. Vilar, “Architectures for SpeechtoSpeech Translation Using FiniteState Models,” Proc. Workshop on SpeechtoSpeech Translation: Algorithms and Systems, pp. 3944, July 2002.
[33] A. Molina and F. Pla, “Shallow Parsing Using Specialized HMMs,” J. Machine Learning Research, vol. 2, pp. 559594, Mar. 2002.
[34] H. Bunke and T. Caelli, Hidden Markov Models Applications in Computer Vision, Series in Machine Perception and Artificial Intelligence, vol. 45. World Scientific, 2001.
[35] R. Llobet, A.H. Toselli, J.C. PerezCortes, and A. Juan, “ComputerAided Prostate Cancer Detection in Ultrasonographic Images,” Proc. First Iberian Conf. Pattern Recognition and Image Analysis, pp. 411419, 2003.
[36] Y. Bengio, V.P. Lauzon, and R. Ducharme, “Experiments on the Application of IOHMMs to Model Financial Returns Series,” IEEE Trans. Neural Networks, vol. 12, no. 1, pp. 113123, 2001.
[37] F. Casacuberta, “Some Relations among Stochastic Finite State Networks Used in Automatic Speech Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 691695, July 1990.
[38] J. Goodman, “A Bit of Progress in Language Modeling,” technical report, Microsoft Research, 2001.
[39] D. McAllester and R.E. Schapire, “On the Convergence Rate of GoodTuring Estimators,” Proc. 13th Ann. Conf. Computer Learning Theory, pp. 16, 2000.
[40] M. Mohri, F. Pereira, and M. Riley, “The Design Principles of a Weighted FiniteState Transducer Library,” Theoretical Computer Science, vol. 231, pp. 1732, 2000.
[41] R. Chaudhuri and S. Rao, “Approximating Grammar Probabilities: Solution to a Conjecture,” J. Assoc. Computing Machinery, vol. 33, no. 4, pp. 702705, 1986.
[42] C.S. Wetherell, “Probabilistic Languages: A Review and Some Open Questions,” Computing Surveys, vol. 12, no. 4, 1980.
[43] F. Casacuberta, “Probabilistic Estimation of Stochastic Regular SyntaxDirected Translation Schemes,” Proc. Spanish Symp. Pattern Recognition and Image Analysis, R. Moreno, ed., pp. 201297, 1995.
[44] F. Casacuberta, “Maximum Mutual Information and Conditional Maximum Likelihood Estimation of Stochastic Regular SyntaxDirected Translation Schemes,” Proc. Third Int'l Colloquium Grammatical Inference: Learning Syntax from Sentences, pp. 282291, 1996.
[45] D. Picó and F. Casacuberta, “A StatisticalEstimation Method for Stochastic FiniteState Transducers Based on Entropy Measures,” Proc. Joint Int'l Assoc. Pattern Recognition Workshops Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition, pp. 417426, 2000.
[46] E.M. Gold, “Language Identification in the Limit,” Information and Control, vol. 10, no. 5, pp. 447474, 1967.
[47] E.M. Gold, “Complexity of Automaton Identification from Given Data,” Information and Control, vol. 37, pp. 302320, 1978.
[48] L.G. Valiant, “A Theory of the Learnable,” Comm. Assoc. Computing Machinery, vol. 27, no. 11, pp. 11341142, 1984.
[49] L. Pitt and M. Warmuth, “The Minimum Consistent DFA Problem Cannot be Approximated within Any Polynomial,” J. Assoc. Computing Machinery, vol. 40, no. 1, pp. 95142, 1993.
[50] F. Denis, C. d'Halluin, and R. Gilleron, “PAC Learning with Simple Examples,” Proc. 13th Symp. Theoretical Aspects of Computer Science, pp. 231242, 1996.
[51] F. Denis and R. Gilleron, “PAC Learning under Helpful Distributions,” Algorithmic Learning Theory, 1997.
[52] R. Parekh and V. Honavar, “Learning DFA from Simple Examples,” Proc. Workshop Automata Induction, Grammatical Inference, and Language Acquisition, 1997.
[53] J.J. Horning, “A Procedure for Grammatical Inference,” Information Processing, vol. 71, pp. 519523, 1972.
[54] D. Angluin, “Identifying Languages from Stochastic Examples,” Technical Report YALEU/DCS/RR614, Yale Univ., Mar. 1988.
[55] S. Kapur and G. Bilardi, “Language Learning from Stochastic Input,” Proc. Fifth Conf. Computational Learning Theory, pp. 303310, July 1992.
[56] N. Abe and M. Warmuth, “On the Computational Complexity of Approximating Distributions by Probabilistic Automata,” Proc. Third Workshop Computational Learning Theory, pp. 5266, 1998.
[57] R. Carrasco and J. Oncina, “Learning Deterministic Regular Grammars from Stochastic Samples in Polynomial Time,” Theoretical Informatics and Applications, vol. 33, no. 1, pp. 120, 1999.
[58] A. Clark and F. Thollard, “PacLearnability of Probabilistic Deterministic Finite State Automata,” J. Machine Learning Research, vol. 5, pp. 473497, May 2004.
[59] C. de la Higuera and F. Thollard, “Identification in the Limit with Probability One of Stochastic Deterministic Finite Automata,” Proc. Fifth Int'l Colloquium Grammatical Inference: Algorithms and Applications, pp. 1524. 2000.
[60] R. Carrasco and J. Oncina, “Learning Stochastic Regular Grammars by Means of a State Merging Method,” Proc. Second Int'l Colloquium Grammatical Inference, pp. 139150, 1994.
[61] F. Thollard, P. Dupont, and C. de la Higuera, “Probabilistic DFA Inference Using KullbackLeibler Divergence and Minimality,” Proc. 17th Int'l Conf. Machine Learning, pp. 975982, 2000.
[62] F. Thollard and A. Clark, “Shallow Parsing Using Probabilistic Grammatical Inference,” Proc. Sixth Int'l Colloquium Grammatical Inference, pp. 269282, Sept. 2002.
[63] C. Kermorvant and P. Dupont, “Stochastic Grammatical Inference with Multinomial Tests,” Proc. Sixth Int'l Colloquium Grammatical Inference: Algorithms and Applications, pp. 149160, 2002.
[64] M. YoungLai and F.W. Tompa, “Stochastic Grammatical Inference of Text Database Structure,” Machine Learning J., vol. 40, no. 2, pp. 111137, 2000.
[65] P. García, E. Vidal, and F. Casacuberta, “Local Languages, the Succesor Method, and a Step Towards a General Methodology for the Inference of Regular Grammars,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 6, pp. 841845, June 1987.
[66] A. Orlitsky, N.P. Santhanam, and J. Zhang, “Always Good Turing: Asymptotically Optimal Probability Estimation,” Proc. 44th Ann. IEEE Symp. Foundations of Computer Science, p. 179, Oct. 2003.
[67] S. Katz, “Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recognizer,” IEEE Trans. Acoustic, Speech and Signal Processing, vol. 35, no. 3, pp. 400401, 1987.
[68] R. Kneser and H. Ney, “Improved BackingOff for mGram Language Modeling,” IEEE Int'l Conf. Acoustics, Speech and Signal Processing, vol. 1, pp. 181184, 1995.
[69] S.F. Chen and J. Goodman, “An Empirical Study of Smoothing Techniques for Language Modeling,” Proc. 34th Ann. Meeting of the Assoc. for Computational Linguistics, pp. 310318, 1996.
[70] F. Thollard, “Improving Probabilistic Grammatical Inference Core Algorithms with PostProcessing Techniques,” Proc. 18th Int'l Conf. Machine Learning, pp. 561568, 2001.
[71] D. Llorens, J.M. Vilar, and F. Casacuberta, “Finite State Language Models Smoothed Using nGrams,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 16, no. 3, pp. 275289, 2002.
[72] J. Amengual, A. Sanchis, E. Vidal, and J. Benedí, “Language Simplification through ErrorCorrecting and Grammatical Inference Techniques,” Machine Learning J., vol. 44, no. 1, pp. 143159, 2001.
[73] P. Dupont and L. Chase, “Using Symbol Clustering to Improve Probabilistic Automaton Inference,” Proc. Fourth Int'l Colloquium Grammatical Inference, pp. 232243, 1998.
[74] R. Kneser and H. Ney, “Improved Clustering Techniques for ClassBased Language Modelling,” Proc. European Conf. Speech Comm. and Technology, pp. 973976, 1993.
[75] C. Kermorvant and C. de la Higuera, “Learning Languages with Help,” Proc. Int'l Colloquium Grammatical Inference, vol. 2484, 2002.
[76] L. Breiman, “Bagging Predictors,” Machine Learning J., vol. 24, no. 2, pp. 123140, 1996.
[77] S. Bangalore and G. Riccardi, “Stochastic FiniteState Models for Spoken Language Machine Translation,” Proc. Workshop Embedded Machine Translation Systems, North Am. Chapter Assoc. for Computational Linguistics, pp. 5259, May 2000.
[78] S. Bangalore and G. Ricardi, “A FiniteState Approach to Machine Translation,” Proc. North Am. Chapter Assoc. for Computational Linguistics, May 2001.
[79] J. Oncina, P. García, and E. Vidal, “Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 5, pp. 448458, May 1993.
[80] J.M. Vilar, “Improve the Learning of Subsequential Transducers by Using Alignments and Dictionaries,” Proc. Fifth Int'l Colloquium Grammatical Inference: Algorithms and Applications, pp. 298312, 2000.
[81] F. Casacuberta, “Inference of FiniteState Transducers by Using Regular Grammars and Morphisms,” Proc. Fifth Int'l Colloquium Grammatical Inference: Algorithms and Applications, pp. 114, 2000.
[82] F. Casacuberta, H. Ney, F.J. Och, E. Vidal, J.M. Vilar, S. Barrachina, I. GarcíaVarea, D. Llorens, C. Martínez, S. Molau, F. Nevado, M. Pastor, D. Picó, A. Sanchis, and C. Tillmann, “Some Approaches to Statistical and FiniteState SpeechtoSpeech Translation,” Computer Speech and Language, 2003.
[83] F. Casacuberta and E. Vidal, “Machine Translation with Inferred Stochastic FiniteState Transducers,” Computational Linguistics, vol. 30, no. 2, pp. 205225, 2004.
[84] M. Mohri, “FiniteState Transducers in Language and Speech Processing,” Computational Linguistics, vol. 23, no. 3, pp. 269311, 1997.
[85] M. Mohri, F. Pereira, and M. Riley, “Weighted FiniteState Transducers in Speech Recognition,” Computer Speech and Language, vol. 16, no. 1, pp. 6988, 2002.
[86] H. Alshawi, S. Bangalore, and S. Douglas, “Head Transducer Model for Speech Translation and Their Automatic Acquisition from Bilingual Data,” Machine Translation, 2000.
[87] H. Alshawi, S. Bangalore, and S. Douglas, “Learning Dependency Translation Models as Collections of Finite State Head Transducers,” Computational Linguistics, vol. 26, 2000.
[88] F. Casacuberta and C. de la Higuera, “Computational Complexity of Problems on Probabilistic Grammars and Transducers,” Proc. Fifth Int'l Colloquium Grammatical Inference: Algorithms and Applications, pp. 1524, 2000.
[89] F. Casacuberta, E. Vidal, and D. Picó, “Inference of FiniteState Transducers from Regular Languages,” Pattern Recognition, 2004, to appear.
[90] E. Mäkinen, “Inferring Finite Transducers,” Technical Report A19993, Univ. of Tampere, 1999.
[91] E. Vidal, P. García, and E. Segarra, “Inductive Learning of FiniteState Transducers for the Interpretation of Unidimensional Objects,” Structural Pattern Analysis, R. Mohr, T. Pavlidis, and A. Sanfeliu, eds., pp. 1735, 1989.
[92] K. Knight and Y. AlOnaizan, “Translation with FiniteState Devices,” Proc. Proc. Third Conf. Assoc. for Machine Translation in the Americas: Machine Translation and the Information Soup, vol. 1529, pp. 421437, 1998.
[93] J. Eisner, “Parameter Estimation for Probabilistic FiniteState Transducers,” Proc. 40th Ann. Meeting Assoc. Computational Linguistics, July 2002.
[94] D. Llorens, “Suavizado de Autómatas y Traductores Finitos Estocásticos,” PhD dissertation, Univ. Politècnica de València, 2000.
[95] M.J. Nederhoff, “Practical Experiments with Regular Approximation of ContextFree Languages,” Computational Linguistics, vol. 26, no. 1, 2000.
[96] M. Mohri and M.J. Nederhof, “Regular Approximations of ContextFree Grammars through Transformations,” Robustness in Language and Speech Technology, J.C. Junqua and G. van Noord, eds., pp. 252261. Kluwer Academic Publisher, Springer Verlag, 2000.
[97] K. Lari and S. Young, “The Estimation of Stochastic ContextFree Grammars Using the InsideOutside Algorithm,” Computer Speech and Language, no. 4, pp. 3556, 1990.
[98] J. Sánchez and J. Benedí, “Consistency of Stocastic Context— Free Grammars from Probabilistic Estimation Based on Growth Transformation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 10521055, Sept. 1997.
[99] J. Sánchez, J. Benedí, and F. Casacuberta, “Comparison between the InsideOutside Algorithm and the Viterbi Algorithm for Stochastic ContextFree Grammars,” Proc. Sixth Int'l Workshop Advances in Syntactical and Structural Pattern Recognition, pp. 5059, 1996.
[100] Y. Takada, “Grammatical Inference for Even Linear Languages Based on Control Sets,” Information Processing Letters, vol. 28, no. 4, pp. 193199, 1988.
[101] T. Koshiba, E. Mäkinen, and Y. Takada, “Learning Deterministic Even Linear Languages from Positive Examples,” Theoretical Computer Science, vol. 185, no. 1, pp. 6379, 1997.
[102] T. Koshiba, E. Mäkinen, and Y. Takada, “Inferring Pure ContextFree Languages from Positive Data,” Acta Cybernetica, vol. 14, no. 3, pp. 469477, 2000.
[103] Y. Sakakibara, “Learning ContextFree Grammars from Structural Data in Polynomial Time,” Theoretical Computer Science, vol. 76, pp. 223242, 1990.
[104] F. Maryanski and M.G. Thomason, “Properties of Stochastic SyntaxDirected Translation Schemata,” Int'l J. Computer and Information Science, vol. 8, no. 2, pp. 89110, 1979.
[105] A. Fred, “Computation of Substring Probabilities in Stochastic Grammars,” Proc. Fifth Int'l Colloquium Grammatical Inference: Algorithms and Applications, pp. 103114, 2000.
[106] V. Balasubramanian, “Equivalence and Reduction of Hidden Markov Models,” Technical Report AITR1370, Mass. Inst. of Tech nology, 1993.