On the role of update constraints and text-types in iterative learning
暂无分享,去创建一个
[1] James S. Royer,et al. Subrecursive Programming Systems , 1994, Progress in Theoretical Computer Science.
[2] John Case,et al. When unlearning helps , 2008, Inf. Comput..
[3] Steffen Lange,et al. Variants of iterative learning , 2003, Theor. Comput. Sci..
[4] John Case,et al. Infinitary self-reference in learning theory , 1994, J. Exp. Theor. Artif. Intell..
[5] Klaus P. Jantke. Monotonic and non-monotonic inductive inference , 2009, New Generation Computing.
[6] Dana Angluin,et al. Inductive Inference of Formal Languages from Positive Data , 1980, Inf. Control..
[7] Thomas Zeugmann,et al. Monotonic Versus Nonmonotonic Language Learning , 1991, Nonmonotonic and Inductive Logic.
[8] John Case,et al. Machine Inductive Inference and Language Identification , 1982, ICALP.
[9] Daniel N. Osherson,et al. Criteria of Language Learning , 1982, Inf. Control..
[10] Mark A. Fulk. Prudence and Other Conditions on Formal Language Learning , 1990, Inf. Comput..
[11] John Case,et al. Optimal Language Learning , 2008, ALT.
[12] Sanjay Jain,et al. On Conservative Learning of Recursively Enumerable Languages , 2013, CiE.
[13] Timo Kötzing,et al. Abstraction and complexity in computational learning in the limit , 2009 .
[14] Thomas Zeugmann,et al. Incremental Learning from Positive Data , 1996, J. Comput. Syst. Sci..
[15] John Case,et al. Periodicity in generations of automata , 1974, Mathematical systems theory.
[16] Sanjay Jain,et al. On the Role of Update Constraints and Text-Types in Iterative Learning , 2014, ALT.
[17] John Case,et al. The Power of Vacillation in Language Learning , 1999, SIAM J. Comput..
[18] Steffen Lange,et al. Incremental learning of approximations from positive data , 2004, Inf. Process. Lett..
[19] Daniel N. Osherson,et al. Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists , 1990 .
[20] Manuel Blum,et al. Toward a Mathematical Theory of Inductive Inference , 1975, Inf. Control..
[21] J. Case,et al. Subrecursive Programming Systems: Complexity & Succinctness , 1994 .
[22] Timo Kötzing. A Solution to Wiehagen's Thesis , 2014, STACS.
[23] John Case,et al. Results on memory-limited U-shaped learning , 2007, Inf. Comput..
[24] Eliana Minicozzi,et al. Some Natural Properties of Strong-Identification in Inductive Inference , 1976, Theor. Comput. Sci..
[25] John Case,et al. Strongly Non-U-Shaped Learning Results by General Techniques , 2010, COLT.
[26] Klaus P. Jantke,et al. Monotonic and Nonmonotonic Inductive Inference of Functions and Patterns , 1990, Nonmonotonic and Inductive Logic.
[27] Rolf Wiehagen. A Thesis in Inductive Inference , 1990, Nonmonotonic and Inductive Logic.
[28] Thomas Zeugmann,et al. Learning indexed families of recursive languages from positive data: A survey , 2008, Theor. Comput. Sci..
[29] Sanjay Jain,et al. Learning without coding , 2013, Theor. Comput. Sci..
[30] Robert H. Sloan,et al. BOOK REVIEW: "SYSTEMS THAT LEARN: AN INTRODUCTION TO LEARNING THEORY, SECOND EDITION", SANJAY JAIN, DANIEL OSHERSON, JAMES S. ROYER and ARUN SHARMA , 2001 .
[31] Jr. Hartley Rogers. Theory of Recursive Functions and Effective Computability , 1969 .
[32] John Case,et al. U-shaped, iterative, and iterative-with-counter learning , 2007, Machine Learning.
[33] Steffen Lange,et al. On the power of incremental learning , 2002, Theor. Comput. Sci..
[34] E. Mark Gold,et al. Language Identification in the Limit , 1967, Inf. Control..
[35] Rolf Wiehagen. Limes-Erkennung rekursiver Funktionen durch spezielle Strategien , 1975, J. Inf. Process. Cybern..