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Deniz Yuret and Ferhan Türe. 2006. Learning Morphological Disambiguation Rules for Turkish. In HLT-NAACL 06, June. [ML, Morphology, ai.ku] pdf url pdf ppt google scholar
Ian H. Witten and Eibe Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. (Ercument-2011-11-01). [ML, book.ml, missing, ebook] pdf google scholar books
Volkan Kurt. 2005. Protein Structure Prediction Using Decision Lists. Koç University. [ML] google scholar
D.A. Newlands and Geoffrey I. Webb. 2004. Alternative Strategies for Decision List Construction. In Proceedings of the Fourth Data Mining Conference (DM IV 03), pp 265--273. [ML] google scholar
D. J. Newman, S. Hettich, C. L. Blake and C. J. Merz. 1998. UCI Repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences. http://www.ics.uci.edu/$\sim$mlearn/MLRepository.html. [ML] google scholar
G. H. John. 1997. Enhancements to the data mining process. Stanford University. [ML] google scholar
Tom Mitchell. 1997. Machine Learning. McGraw Hill. [ML, book.ml, ebook] pdf google scholar books
Geoffrey I. Webb. 1995. OPUS: An Efficient Admissible Algorithm for Unordered Search. JAIR, vol 3, pp 431--465. [ML] pdf google scholar
Geoffrey I. Webb. 1994. Recent Progress in Learning Decision Lists by Prepending Inferred Rules. In Proceedings of the Second Singapore International Conference on Intelligent Systems (SPICIS '94), pp B280--B285, Singapore. [ML] google scholar
Jeffrey C. Jackson. 1994. An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution. In IEEE Symposium on Foundations of Computer Science, pp 42--53. [ML] google scholar
G. H. John, R. Kohavi and P. Pfleger. 1994. Irrelevant features and the subset selection problem. In Proceedings of the Eleventh International Conference on Machine Learning, pp 121--129, New Brunswick, NJ. San Francisco: Morgan Kaufmann. [ML] google scholar
Andras Farago and Gabor Lugosi. 1993. Strong Universal Consistency of Neural Network Classifiers. IEEE Transactions on Information Theory, vol 39, no 4, pp 1146--1151, July. [ML] google scholar
Geoffrey I. Webb and N. Brkic. 1993. Learning Decision Lists by Prepending Inferred Rules. In Proceedings of the AI 93 Workshop on Machine Learning and Hybrid Systems, pp 6--10, Melbourne. [ML] google scholar
U. M. Fayyad and K. B. Irani. 1993. Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the Workshop on Massive Datasets, Washington, DC. NRC, Committee on Applied and Theoretical Statistics. [ML] google scholar
J Ross Quinlan. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann. [ML] google scholar books
Martin Anthony and Norman Biggs. 1992. Computational Learning Theory: An Introduction, vol 30, pp 1--8. Cambridge University Press. [ML] google scholar
Avrim Blum. 1992. Learning Boolean Functions in an Infinite Attribute Space. Machine Learning, vol 9, no 4, pp 373--386. Kluwer Academic Publishers. [ML] google scholar
H. Almuallim and Thomas G. Dietterich. 1992. Efficient algorithms for identifying relevant features. In Proceedings of the Ninth Canadian Conference on Artificial Intelligence, pp 38--45. [ML] google scholar
Avrim L. Blum and Ronald L. Rivest. 1992. Training a 3-node Neural Network is NP-Complete. Neural Networks, vol 5, pp 117--127. Pergamon Press. [ML] google scholar
D Aha. 1992. Tolerating noisy, irrelevant, and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies, vol 36, no 2, pp 267--287. [ML] google scholar
David W. Aha, Dennis Kibler and Marc K. Albert. 1991. Instance-Based Learning Algorithms. Machine Learning, vol 6, no 1, pp 37--66. [ML] pdf google scholar
H. Almuallim and Thomas G. Dietterich. 1991. Learning with many irrelevant features. In Proceedings of the Ninth National Conference on Artificial Intelligence, pp 547--552. AAAI Press. [ML] google scholar
Eyal Kushilevitz and Yishay Mansour. 1991. Learning Decision Trees using the Fourier Spectrum. In Proceedings of the Twenty-third Annual ACM Symposium on Theory of Computing, pp 455-464. [ML] google scholar
Peter Clark and Robin Boswell. 1991. Rule Induction with CN2: Some Recent Improvements. In Machine Learning -- Proceedings of the Fifth European Conference (EWSL-91), pp 151--163, Berlin. Springer-Verlag. [ML] google scholar
Avrim Blum. 1990. Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain. In IEEE 31st Annual Symposium on Foundations of Computer Science, vol 1, pp 211--218, October. [ML] google scholar
David Haussler. 1990. Probably Approximately Correct Learning. In National Conference on Artificial Intelligence, pp 1101--1108, May. [ML] google scholar
Robert E. Schapire. 1990. The Strength of Weak Learnability. Machien Learning, vol 5, pp 197--227, Boston, MA. Kluwer Academic Publishers. [ML] google scholar
David E. Rumelhart, Geoffrey E. Hinton and Ronald J. Williams. 1990. Learning Internal Representations by Error Propagation. In Readings in Machine Learning, pp 115--137, San Mateo, CA. Kaufmann. [ML] google scholar
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler and Manfred K. Warmuth. 1989. Learnability and the Vapnik-Chervonenkis Dimension. Journal of the ACM, vol 36, no 4, pp 929--965, October. ACM Press. [ML] google scholar
Peter Clark and Tim Niblett. 1989. The CN2 Induction Algorithm. Machine Learning, vol 3, pp 261--283. [ML] google scholar
Eric B. Baum and David Haussler. 1989. What Size Net Gives Valid Generalization?. Neural Computation, vol 1, pp 151--160. Massachussets Institute of Technology. [ML] google scholar
Ronald L. Rivest and Robert E. Schapire. 1989. Inference of Finite Automata Using Homing Sequences (extended abstract). In Proceedings of the Twenty-first Annual ACM Symposium on Theory of Computing, pp 411--420. ACM Press. [ML] google scholar
Nick Littlestone and Manfred K. Warmuth. 1989. The Weighted Majority Algorithm. In IEEE Symposium on Foundations of Computer Science, pp 256--261. [ML] google scholar
David Haussler, Nick Littlestone and Manfred K. Warmuth. 1988. Predicting $(0, 1)$-Functions on Randomly Drawn Points. In 29th Annual Symposium on Foundations of Computer Science, pp 100--109, October. [ML] google scholar
George Cybenko. 1988. Approximation by Superpositions of a Sigmoidal Function, Medford, MA, October. Department of computer Science, Tufts University. [ML] google scholar
Luc Devroye. 1988. Automatic Pattern Recognition: A Study of the Probability of Error. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 10, no 4, pp 530--543, July. [ML] google scholar
George Cybenko. 1988. Continuous Valued Neural Networks with Two Hidden Layers Are Sufficient, Medford, MA. Department of Computer Science, Tufts University. [ML] google scholar
Dana Angluin and Philip Laird. 1988. Learning from Noisy Examples. Machine Learning, vol 2, no 4, pp 343--370. Kluwer Academic Publishers. [ML] google scholar
Michael Kearns and Ming Li. 1988. Learning in the Presence of Malicious Errors. In Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing, pp 267--280. [ML] google scholar
Dana Angluin. 1988. Queries and Concept Learning. Machine Learning, vol 2, pp 319--342. Kluwer Academic Publishers. [ML] google scholar
Nick Littlestone. 1988. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm. Machine Learning, vol 2, no 4, pp 285--318. Kluwer Academic Publishers. [ML] google scholar
Ronald L. Rivest and Robert E. Schapire. 1987. Diversity-based Inference of Finite Automata (extended abstract). In IEEE 28th Annual Symposium on Foundations of Computer Science, pp 78--87, October. [ML] google scholar
Dana Angluin. 1987. Learning k-term DNF Formulas Using Queries and Counterexamples, August. Department of Computer Science, Yale University. [ML] google scholar
Terrence J. Sejnowski and Charles R. Rosenberg. 1987. Parallel Networks that Learn to Pronounce English Text. Complex Systems, vol 1, pp 145--168. Complex Systems Publications, Inc.. [ML] google scholar
Michael Kearns, Ming Li, Leonard Pitt and Leslie G. Valiant. 1987. On the Learnability of Boolean Formulae. In Proceedings of the Nineteenth Annual ACM Conference on Theory of Computing, pp 285--295, New York, NY. ACM Press. [ML] google scholar
Ronald L. Rivest. 1987. Learning Decision Lists. Machine Learning, vol 2, pp 229--246. Kluwer Academic Publishers. [ML] google scholar
Dana Angluin. 1987. Learning Regular Sets from Queries and Counterexamples. Information and Computation, vol 75, pp 87--106. Academic Press. [ML] google scholar
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler and Manfred K. Warmuth. 1987. Occam's Razor. Information Processing Letters, vol 24, pp 377--380. Elsevier Science Publishers. [ML] google scholar
J Ross Quinlan. 1986. Induction of Decision Trees. Machine Learning, vol 1, pp 81--106. Kluwer Academic Publishers. [ML] google scholar
L. R. Rabiner and B. H. Juang. 1986. An Introduction to Hidden Markov Models. IEEE ASSP Magazine, pp 4--16, January. [ML] google scholar
Leslie G. Valiant. 1984. A Theory of the Learnable. Communications of the ACM, vol 27, no 11, pp 1134--1142. ACM Press. [ML] google scholar
Jerome H. Friedman, Jon L. Bentley and Raphael A. Finkel. 1977. An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematical Software, vol 3, no 3, pp 209--226. [ML] google scholar
Richard O. Duda and Peter E. Hart. 1973. Pattern Classification and Scene Analysis, pp 98--105. John Wiley and Sons. [ML] google scholar
Thomas M. Cover. 1973. On Determining the Irrationality og the Mean of a Random Variable. The Annals of Statistics, vol 1, no 5, pp 862--871. [ML] google scholar
F. Rosenblatt. 1958. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, vol 65, no 6, pp 386--408. cit 4607. [ML, perceptron] pdf google scholar
Yasubumi Sakakibara. An Efficient Robust Algorithm for Learning Decision Lists. [ML] google scholar
Matthias Seeger. The Proof of McAllester's PAC-Bayesian Theorem. [ML] pdf google scholar

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