"Computational Learning Theory (COLT) is a reasearch field devoted to studying the design and analysis of algorithms for making predictions about the future based on past experiences. The emphasis in COLT is on rigorous mathematical analysis. As a field with roots in theoretical computer science, COLT is largely concerned with computational and data efficiency. Much of the work in COLT can be traced to Valiant's seminal paper on "A theory of the learnable" (1984) as well as Gold's "Language identification in the limit" (1967). The annual Conference on Computational Learning Theory began in 1988; the European Conference on Computational Learning Theory and the Workshop on Algorithmic Learning Theory were formed soon after. COLT has strongly encouraged interaction with other fields that work on problems of prediction such as applied machine learning, statistics, information theory, pattern recognition and statistical physics, as well as other areas of computer science such as artificial intelligence, complexity theory and cryptography."

Freund and Schapire

Wikipedia: Computational learning theory

- ANTHONY, M. and N. BIGGS, 1997. Computational Learning Theory. [Cited by 268]
- KEARNS, Michael J. and Umesh V. VAZIRANI, 1994. An Introduction to Computational Learning Theory. [Cited by 410]

See also "Proceedings of the (nth) Annual Workshop on Computational Learning Theory"

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*… the fourth annual workshop on Computational learning theory.*[Cited by 20] (1.23/year) - AMARI, S., N. FUJITA and S. SHINOMOTO, 1992. Four types of learning curves.
*Neural Computation.*[Cited by 51] (3.35/year) - ANGLUIN, D., 1987. Learning regular sets from queries and counterexamples.
*Information and Computation.*[Cited by 470] (23.23/year) - ANGLUIN, D., 1992. Computational learning theory: survey and selected bibliography.
*Proceedings of the twenty-fourth annual ACM symposium on ….*[Cited by 96] (6.30/year) - ANTHONY, M., N. BIGGS and J. SHAWE-TAYLOR, 1990. The learnability of formal concepts.
*… the third annual workshop on Computational learning theory.*[Cited by 23] (1.33/year) - ANTHONY, M.H.G., 1992. [BOOK] Computational Learning Theory: An Introduction. books.google.com. [Cited by 321] (21.07/year)
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*Artificial Intelligence.*[Cited by 499] (40.78/year) - BAUM, E.B., 1990. On learning a union of half spaces.
*Journal of Complexity.*[Cited by 33] (1.91/year) - BAUM, E.B., 1990. The perceptron algorithm is fast for nonmalicious distributions.
*Neural Computation.*[Cited by 34] (1.97/year) - BAUM, E.B. and D. HAUSSLER, 1990. What size net gives valid generalization?.
*Neural Computation.*[Cited by 693] (40.21/year) - BENEDEK, G.M. and A. ITAI, 1988. Learnability by fixed distributions.
*… the first annual workshop on Computational learning theory.*[Cited by 78] (4.06/year) - BENVENISTE, A., P. PRIOURET and M. M?TIVIER, 1990. Adaptive algorithms and stochastic approximations - all 2 versions ». Springer-Verlag New York, Inc. New York, NY, USA. [Cited by 417] (24.19/year)
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*AAAI Fall Symposium on Relevance.*[Cited by 18] (1.36/year) - BLUM, A. and M. SINGH, 1990. Learning functions of k terms.
*… the third annual workshop on Computational learning theory.*[Cited by 29] (1.68/year) - BLUM, A. and R.L. RIVEST, 1988. Training a 3-node neural network is NP-complete.
*… the first annual workshop on Computational learning theory.*[Cited by 245] (12.74/year) - BLUM, M. and S. MICALI, 1984. How to generate cryptographically strong sequences of pseudo-random bits.
*SIAM Journal on Computing.*[Cited by 609] (26.21/year) - BLUMER, A.,
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*Proceedings of the 9th Colloquium on Automata, Languages and ….*[Cited by 68] (2.69/year) - CLAUSEN, M.,
*et al.*, 1991. On zero-testing and interpolation of k-sparse multivariate polynomials over finite fields.*Theoretical Computer Science.*[Cited by 44] (2.71/year) - CSISZAR, I. and J.G. KORNER, 1982. Information Theory: Coding Theorems for Discrete Memoryless Systems. Academic Press, Inc. Orlando, FL, USA. [Cited by 733] (29.05/year)
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*Artificial Intelligence.*[Cited by 90] (8.79/year) - DEAN, T. and K. KANAZAWA, 1989. A model for reasoning about persistence and causation.
*Computational Intelligence.*[Cited by 381] (20.89/year) - EHRENFEUCHT, A., D. HAUSSLER and M. KEARNS, 1989. Learning decision trees from random examples needed for learning.
*Information and Computation.*[Cited by 77] (4.22/year) - EISENBERG, B. and R.L. RIVEST, 1990. On the sample complexity of pac-learning using random and chosen examples. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA. [Cited by 21] (1.22/year)
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*Lecture Notes In Computer Science; Vol. 502.*[Cited by 31] (1.91/year) - FREUND, Y., 1995. Boosting a weak learning algorithm by majority.
*Information and Computation.*[Cited by 461] (37.68/year) - FULK, M.A., 1990. Prudence and other conditions on formal language learning.
*Information and Computation.*[Cited by 49] (2.84/year) - FURST, M.L., J.C. JACKSON and S.W. SMITH, 1991. Improved learning of AC 0 functions.
*… the fourth annual workshop on Computational learning theory.*[Cited by 32] (1.97/year) - GASARCH, W.I. and M.G. PLESZKOCH, 1989. Learning via queries to an oracle.
*… the second annual workshop on Computational learning theory.*[Cited by 20] (1.10/year) - GEMAN, S., E. BIENENSTOCK and R. DOURSAT, 1992. Neural networks and the bias/variance dilemma.
*Neural Computation.*[Cited by 1138] (74.69/year) - GILES, C.L.,
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*Theoretical Computer Science.*[Cited by 39] (6.25/year) - HAGERUP, T. and C. R?B, 1990. A guided tour of Chernoff bounds.
*Information Processing Letters.*[Cited by 274] (15.90/year) - HANCOCK, T. and L. HELLERSTEIN, 1991. Learning read-once formulas over fields and extended bases.
*… the fourth annual workshop on Computational learning theory.*[Cited by 22] (1.36/year) - HANCOCK, T. and Y. MANSOUR, 1991. Learning monotone ku DNF formulas on product distributions.
*… the fourth annual workshop on Computational learning theory.*[Cited by 27] (1.66/year) - HANCOCK, T.R., 1991. Learning 2u DNF formulas and ku decision trees.
*… the fourth annual workshop on Computational learning theory.*[Cited by 29] (1.79/year) - HANCOCK, T.R., 1990. Identifying ?-formula decision trees with queries. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA. [Cited by 24] (1.39/year)
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*Artificial Intelligence.*[Cited by 274] (14.24/year) - HAUSSLER, D., 1992. Decision theoretic generalizations of the PAC model for neural net and other learning applications.
*Information and Computation.*[Cited by 400] (26.25/year) - HAUSSLER, D., 1995. Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension.
*Journal of Combinatorial Theory Series A.*[Cited by 93] (7.60/year) - HELMBOLD, D.P. and P.M. LONG, 1991. Tracking drifting concepts using random examples.
*… the fourth annual workshop on Computational learning theory.*[Cited by 21] (1.29/year) - HORNIK, K., 1991. Approximation capabilities of multilayer feedforward networks.
*Neural Networks.*[Cited by 573] (35.29/year) - H?STAD, J., 1987. Computational limitations of small-depth circuits - all 3 versions ». MIT Press Cambridge, MA, USA. [Cited by 177] (8.75/year)
- IBARRA, O.H. and T. JIANG, 1988. Learning regular languages from counterexamples.
*… the first annual workshop on Computational learning theory.*[Cited by 25] (1.30/year) - JAIN, S. and A. SHARMA, 1990. Finite learning by a “team”.
*… the third annual workshop on Computational learning theory.*[Cited by 17] (0.99/year) - JANTKE, K.P., 1991. Monotonic and non-monotonic inductive inference.
*New Generation Computing.*[Cited by 25] (1.54/year) - KEARNS, M. and L. PITT, 1989. A polynomial-time algorithm for learning k-variable pattern languages from examples.
*… the second annual workshop on Computational learning theory.*[Cited by 34] (1.86/year) - KEARNS, M.J. and U.V. VAZIRANI, 1994. [BOOK] An Introduction to Computational Learning Theory. books.google.com. [Cited by 557] (42.08/year)
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*Journal of Computer and System Sciences.*[Cited by 28] (8.65/year) - KOHONEN, T., 1989. Self-organization and associative memory - all 6 versions ». Springer-Verlag New York, Inc. New York, NY, USA. [Cited by 4739] (259.88/year)
- LAIRD, P.D., 1988. Learning from good and bad data - all 2 versions ». Kluwer Academic Publishers Norwell, MA, USA. [Cited by 73] (3.80/year)
- LANGE, S. and R. WIEHAGEN, 1991. Polynomial-time inference of arbitrary pattern languages.
*New Generation Computing.*[Cited by 59] (3.63/year) - LAVRAC, N. and S. DZEROSKI, 1993. Inductive Logic Programming: Techniques and Applications - all 2 versions ». Routledge New York, NY, 10001. [Cited by 457] (32.10/year)
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*… the fourth annual workshop on Computational learning theory.*[Cited by 92] (5.67/year) - LITTLESTONE, N., 1990. Mistake bounds and logarithmic linear-threshold learning algorithms. [Cited by 118] (6.85/year)
- LITTLESTONE, N., 1989. From on-line to batch learning.
*… the second annual workshop on Computational learning theory.*[Cited by 56] (3.07/year) - LLOYD, J.W., 1987. Foundations of logic programming - all 7 versions ». Springer-Verlag New York, Inc. New York, NY, USA. [Cited by 3150] (155.67/year)
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*… the fourth annual workshop on Computational learning theory.*[Cited by 30] (1.85/year) - NEWELL, A., 1990. Unified theories of cognition - all 2 versions ». Harvard University Press Cambridge, MA, USA. [Cited by 2070] (120.10/year)
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*Theoretical Computer Science.*[Cited by 190] (11.70/year) - PITT, L., 1989. Inductive Inference, DFAs, and Computational Complexity. Springer-Verlag London, UK. [Cited by 108] (5.92/year)
- PITT, L. and C.H. SMITH, 1988. Probability and plurality for aggregations of learning machines.
*Information and Computation.*[Cited by 54] (2.81/year) - PITT, L. and M.K. WARMUTH, 1990. Prediction-preserving reducibility.
*Journal of Computer and System Sciences.*[Cited by 88] (5.11/year) - QUINLAN, J.R. and R.L. RIVEST, 1989. Inferring decision trees using the minimum description length principle.
*Information and Computation.*[Cited by 392] (21.50/year) - RAGHAVAN, V. and S.R. SCHACH, 1990. Learning switch configurations.
*… the third annual workshop on Computational learning theory.*[Cited by 17] (0.99/year) - RENEGAR, J., 1992. On the computational complexity and geometry of the first-order theory of the reals. Par I: ….
*Journal of Symbolic Computation.*[Cited by 304] (19.95/year) - RISSANEN, J., 1989. Stochastic Complexity in Statistical Inquiry Theory - all 2 versions ». World Scientific Publishing Co., Inc. River Edge, NJ, USA. [Cited by 1226] (67.23/year)
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*… the first annual workshop on Computational learning theory.*[Cited by 27] (1.40/year) - ROGERS, H., 1987. Theory of recursive functions and effective computability - all 3 versions ». MIT Press Cambridge, MA, USA. [Cited by 1551] (76.65/year)
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*Information Processing Letters.*[Cited by 21] (1.29/year) - SAMUEL, A.L., 1995. Some studies in machine learning using the game of checkers.
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*Lecture Notes In Computer Science.*[Cited by 65] (5.31/year)