Linked bibliography for the SEP article "Formal Learning Theory" by Oliver Schulte |
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If everything goes well, this page should display the bibliography of the aforementioned article as it appears in the Stanford Encyclopedia of Philosophy, but with links added to PhilPapers records and Google Scholar for your convenience. Some bibliographies are not going to be represented correctly or fully up to date. In general, bibliographies of recent works are going to be much better linked than bibliographies of primary literature and older works. Entries with PhilPapers records have links on their titles. A green link indicates that the item is available online at least partially.
This experiment has been authorized by the editors of the Stanford Encyclopedia of Philosophy. The original article and bibliography can be found here.
- Apsitis, K., 1994. “Derived sets and inductive inference”, in: Proceedings of ALT, S. Arikawa, K.P. Jantke (Eds.), Springer, Berlin, Heidelberg, 1994, pp. 26–39. (Scholar)
- Bub, J., 1994. ‘Testing Models of Cognition Through the Analysis of Brain-Damaged Performance’, British Journal for the Philosophy of Science, 45: 837–55. (Scholar)
- Chart, D., 2000. ‘Schulte and Goodman's Riddle’, British Journal for the Philosophy of Science, 51: 837–55. (Scholar)
- Earman, J., 1992. Bayes or Bust?. Cambridge, Mass.: MIT Press. (Scholar)
- Feynman, R., 1965. The Character of Physical Law, Cambridge, Mass.: MIT Press, 19th edition, 1990. (Scholar)
- Friend, M. and N. Goethe and V. Harazinov (eds.), 2007. Induction, Algorithmic Learning Theory, and Philosophy, Dordrecht: Springer, pp. 111–144. (Scholar)
- Ford, K., 1963. The World of Elementary Particles, New York: Blaisdell Publishing. (Scholar)
- Gärdenfors, P., 1988. Knowledge In Flux: modeling the dynamics of epistemic states, Cambridge, Mass.: MIT Press. (Scholar)
- Glymour, C., 1991. ‘The Hierarchies of Knowledge and the Mathematics of Discovery’, Minds and Machines, 1: 75–95. (Scholar)
- –––, 1994. ‘On the Methods of Cognitive Neuropsychology’, British Journal for the Philosophy of Science 45: 815–35. (Scholar)
- Glymour, C. and Kelly, K., 1992. ‘Thoroughly Modern Meno’, in: Inference, Explanation and Other Frustrations, ed. John Earman, University of California Press. (Scholar)
- Gold, E., 1967. ‘Language Identification in the Limit’, Information and Control 10: 447–474. (Scholar)
- Goodman, N., 1983. Fact, Fiction and Forecast. Cambridge, MA: Harvard University Press. (Scholar)
- Harrell, M., 2000. Chaos and Reliable Knowledge, Ph.D. Thesis, University of California at San Diego. (Scholar)
- Harman, G. and Kulkarni, S., 2007. Reliable Reasoning: Induction and Statistical Learning Theory. The MIT Press, London, England. (Scholar)
- Jain, S., et al., 1999. Systems That Learn 2nd ed. Cambridge, MA: MIT Press. (Scholar)
- James, W., 1982. ‘The Will To Believe’, in Pragmatism, ed. H.S. Thayer. Indianapolis: Hackett. (Scholar)
- Juhl, C., 1997. ‘Objectively Reliable Subjective Probabilities’, Synthese, 109: 293–309. (Scholar)
- Kelly, K., 1996. The Logic of Reliable Inquiry, Oxford: Oxford University Press. (Scholar)
- –––, 1999. ‘ Iterated Belief Revision, Reliability, and Inductive Amnesia’, Erkenntnis, 50: 11–58. (Scholar)
- –––, 2000. ‘The Logic of Success’, British Journal for the Philosophy of Science, 51(4): 639–660. (Scholar)
- –––, 2007a. ‘How Simplicity Helps You Find the Truth Without Pointing at it’, in Induction, Algorithmic Learning Theory, and Philosophy, eds. M. Friend, N. Goethe and V. Harazinov. Dordrecht: Springer, pp. 111–144. (Scholar)
- –––, 2007b. ‘Ockham's Razor, Truth, and Information’, in n Handbook of the Philosophy of Information eds. J. van Behthem and P. Adriaans, Dordrecht: Elsevier, 2008. (Scholar)
- –––, 2010. “Simplicity, Truth, and Probability”, in Handbook for the Philosophy of Statistics, Prasanta S. Bandyopadhyay and Malcolm Forster eds., Dordrecht: Elsevier. (Scholar)
- Kelly, K., and Schulte, O., 1995. ‘The Computable Testability of Theories Making Uncomputable Predictions’, Erkenntnis, 43: 29–66. (Scholar)
- Kelly, K., Schulte, O. and Juhl, C., 1997. ‘Learning Theory and the Philosophy of Science’, Philosophy of Science, 64: 245–67. (Scholar)
- Kelly, K., Schulte, O. and Hendricks, V., 1995. ‘Reliable Belief Revision’. Proceedings of the XII Joint International Congress for Logic, Methodology and the Philosophy of Science. (Scholar)
- Kuhn, T., 1970. The Structure of Scientific Revolutions. Chicago: University of Chicago Press. (Scholar)
- Luo, W. and Schulte O., 2006. “Mind Change Efficient Learning”, in Logic and Computation, 204: 989–1011. (Scholar)
- Martin, E. and Osherson, D., 1998. Elements of Scientific Inquiry. Cambridge, MA: MIT Press. (Scholar)
- Ne'eman, Y. and Kirsh, Y., 1983. The Particle Hunters, Cambridge: Cambridge University Press. (Scholar)
- Omnes, R., 1971. Introduction to Particle Physics, London, New York: Wiley Interscience. (Scholar)
- Putnam, H., 1963. “Degree of Confirmation and Inductive Logic”, in The Philosophy of Rudolf Carnap, ed. P.a. Schilpp, La Salle, Ill: Open Court. (Scholar)
- Putnam, H., 1965. “Trial and Error Predicates and the Solution to a Problem of Mostowski”, in The Journal of Symbolic Logic, 30(1): 49–57. (Scholar)
- Quine, W., 1951. ‘Two Dogmas of Empiricism’, Philosophical Review, 60: 20–43. (Scholar)
- Salmon, W., 1991. ‘Hans Reichenbach's Vindication of Induction,’ Erkenntnis, 35: 99–122. (Scholar)
- Schulte, O., 1999a. ‘Means-Ends Epistemology’, The British Journal for the Philosophy of Science, 50: 1–31. (Scholar)
- –––, 1999b. ‘The Logic of Reliable and Efficient Inquiry’, Journal of Philosophical Logic, 28: 399–438. (Scholar)
- –––, 2000. ‘Inferring Conservation Principles in Particle Physics: A Case Study in the Problem of Induction’, The British Journal for the Philosophy of Science, 51: 771–806. (Scholar)
- –––, 2008. ‘The Co-Discovery of Conservation Laws and Particle Families’, O. Schulte (2008). In Studies in History and Philosophy of Modern Physics, 39(2): 288–314. (Scholar)
- Schulte, O., 2009. “Simultaneous Discovery of Conservation Laws and Hidden Particles With Smith Matrix Decomposition”, in Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 1481-1487. (Scholar)
- Schulte, O. and Drew, M.S., 2010. “Learning Conservation Laws Via Matrix Search”, Proceedings of the 13th Conference on Discovery Science, pp. 236–250, Springer LNAI 6332. (Scholar)
- Schulte, O., and Juhl, C., 1996. ‘Topology as Epistemology’, The Monist, 79(1): 141–147. (Scholar)
- Schulte, O., Luo, W., and Greiner, R., 2007. ‘Mind Change Optimal Learning of Bayes Net Structure’, in Proceedings of the 20th Annual Conference on Learning Theory (COLT), San Diego, CA, June 12–15. (Scholar)
- Sklar, L., 1975. ‘Methodological Conservatism’, Philosophical Review, LXXXIV: 374–400. (Scholar)
- Spirtes, P., Glymour, C., Scheines, R., 2000. Causation, prediction, and search, Cambridge, MA: MIT Press. (Scholar)
- Steel, D., 2009. “Testability and Ockham's Razor: How Formal and Statistical Learning Theory Converge in the New Riddle of Induction,” Journal of Philosophical Logic, 38: 471–489. (Scholar)
- Valiant, L. G., 1984. “A theory of the learnable”, Proceedings of the sixteenth annual ACM symposium on Theory of computing, 436–445, ACM Press.
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