Moore, A. W., and M. S. Lee. 1994. Efficient algorithms for minimizing cross vali-
dation error. In W. W. Cohen and H. Hirsh, editors, Proceedings of the Eleventh
International Conference on Machine Learning, New Brunswick, NJ. San
Francisco: Morgan Kaufmann, pp. 190–198.
———. 1998. Cached sufficient statistics for efficient machine learning with large
datasets. Journal Artificial Intelligence Research 8:67–91.
Moore, A. W., and D. Pelleg. 2000. X-means: Extending k-means with efficient
estimation of the number of clusters. In P. Langley, editor, Proceedings of the
Seventeenth International Conference on Machine Learning, Stanford, CA. San
Francisco: Morgan Kaufmann, pp. 727–734.
Nadeau, C., and Y. Bengio. 2003. Inference for the generalization error. Machine
Learning 52(3):239–281.
Nahm, U. Y., and R. J. Mooney. 2000. Using information extraction to aid the dis-
covery of prediction rules from texts. Proceedings of the Workshop on Text
Mining at the Sixth International Conference on Knowledge Discovery and Data
Mining, Boston, MA, pp. 51–58.
Nie, N. H., C. H. Hull, J. G. Jenkins, K. Steinbrenner, and D. H. Bent. 1970. Statis-
tical package for the social sciences. New York: McGraw Hill.
Nigam, K., and R. Ghani. 2000. Analyzing the effectiveness and applicability of co-
training. Proceedings of the Ninth International Conference on Information and
Knowledge Management, McLean, VA. New York: ACM, pp. 86–93.
Nigam, K., A. K. McCallum, S. Thrun, and T. M. Mitchell. 2000. Text classification from
labeled and unlabeled documents using EM. Machine Learning 39(2/3):103–134.
Nilsson, N. J. 1965. Learning machines. New York: McGraw Hill.
Omohundro, S. M. 1987. Efficient algorithms with neural network behavior. Journal
of Complex Systems 1(2):273–347.
Paynter, G. W. 2000. Automating iterative tasks with programming by demonstration.
PhD Dissertation, Department of Computer Science, University of Waikato,
New Zealand.
Piatetsky-Shapiro, G., and W. J. Frawley, editors. 1991. Knowledge discovery in data-
bases. Menlo Park, CA: AAAI Press/MIT Press.
Platt, J. 1998. Fast training of support vector machines using sequential minimal
optimization. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in
kernel methods: Support vector learning. Cambridge, MA: MIT Press.
Provost, F., and T. Fawcett. 1997. Analysis and visualization of classifier perform-
ance: Comparison under imprecise class and cost distributions. In D.
R E F E R E N C E S
4 9 9
P088407-REF.qxd 4/30/05 11:24 AM Page 499
Heckerman, H. Mannila, D. Pregibon, and R. Uthurusamy, editors,
Proceedings of the Third International Conference on Knowledge Discovery and
Data Mining, Huntington Beach, CA. Menlo Park, CA: AAAI Press.
Pyle, D. 1999. Data preparation for data mining. San Francisco: Morgan Kaufmann.
Quinlan, J. R. 1986. Induction of decision trees. Machine Learning 1(1):81–106.
———. 1992. Learning with continuous classes. In N. Adams and L. Sterling,
editors, Proceedings of the Fifth Australian Joint Conference on Artificial Intel-
ligence, Hobart, Tasmania. Singapore: World Scientific, pp. 343–348.
———. 1993. C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann.
Rennie, J. D. M., L. Shih, J. Teevan, and D. R. Karger. 2003. Tackling the poor assump-
tions of Naïve Bayes text classifiers. In T. Fawcett and N. Mishra, editors,
Proceedings of the Twentieth International Conference on Machine Learning,
Washington, DC. Menlo Park, CA: AAAI Press, pp. 616–623.
Ricci, F., and D. W. Aha. 1998. Error-correcting output codes for local learners. In
C. Nedellec and C. Rouveird, editors, Proceedings of the European Conference on
Machine Learning, Chemnitz, Germany. Berlin: Springer-Verlag, pp. 280–291.
Richards, D., and P. Compton. 1998. Taking up the situated cognition challenge
with ripple-down rules. International Journal of Human-Computer Studies
49(6):895–926.
Ripley, B. D. 1996. Pattern recognition and neural networks. Cambridge, UK:
Cambridge University Press.
Rissanen, J. 1985. The minimum description length principle. In S. Kotz and N. L.
Johnson, editors, Encyclopedia of Statistical Sciences, Vol. 5. New York: John
Wiley, pp. 523–527.
Rousseeuw, P. J., and A. M. Leroy. 1987. Robust regression and outlier detection. New
York: John Wiley.
Sahami, M., S. Dumais, D. Heckerman, and E. Horvitz. 1998. A Bayesian approach
to filtering junk email. In Proceedings of the AAAI-98 Workshop on Learning
for Text Categorization, Madison, WI. Menlo Park, CA: AAAI Press, pp. 55–62.
Saitta, L., and F. Neri. 1998. Learning in the “real world.” Machine Learning
30(2/3):133–163.
Salzberg, S. 1991. A nearest hyperrectangle learning method. Machine Learning
6(3):251–276.
Schapire, R. E., Y. Freund, P. Bartlett, and W. S. Lee. 1997. Boosting the margin: A
new explanation for the effectiveness of voting methods. In D. H. Fisher,
5 0 0
R E F E R E N C E S
P088407-REF.qxd 4/30/05 11:24 AM Page 500
editor, Proceedings of the Fourteenth International Conference on Machine
Learning, Nashville, TN. San Francisco: Morgan Kaufmann, pp. 322–330.
Scheffer, T. 2001. Finding association rules that trade support optimally against con-
fidence. In L. de Raedt and A. Siebes, editors, Proceedings of the Fifth European
Conference on Principles of Data Mining and Knowledge Discovery, Freiburg,
Germany. Berlin: Springer-Verlag, pp. 424–435.
Schölkopf, B., and A. J. Smola. 2002. Learning with kernels: Support vector machines,
regularization, optimization, and beyond. Cambridge, MA: MIT Press.
Schölkopf, B., P. Bartlett, A. J. Smola, and R. Williamson. 1999. Shrinking the tube:
A new support vector regression algorithm. Advances in Neural Information
Processing Systems, Vol. 11. Cambridge, MA: MIT Press, pp. 330–336.
Sebastiani, F. 2002. Machine learning in automated text categorization. ACM
Computing Surveys 34(1):1–47.
Seeger, M. 2001. Learning with labeled and unlabeled data. Technical Report,
Institute for Adaptive and Neural Computation, University of Edinburgh, UK.
Seewald, A. K. 2002. How to make stacking better and faster while also taking
care of an unknown weakness. Proceedings of the Nineteenth International
Conference on Machine Learning, Sydney, Australia. San Francisco: Morgan
Kaufmann, pp. 54–561.
Seewald, A. K., and J. Fürnkranz. 2001. An evaluation of grading classifiers. In F.
Hoffmann, D. J. Hand, N. M. Adams, D. H. Fisher, and G. Guimarães, editors,
Proceedings of the Fourth International Conference on Advances in Intelligent
Data Analysis, Cascais, Portugal. Berlin: Springer-Verlag, pp.115–124.
Shafer, R., R. Agrawal, and M. Metha. 1996. SPRINT: A scalable parallel classifier for
data mining. In T. M. Vijayaraman, A. P. Buchmann, C. Mohan, and N. L. Sarda,
editors, Proceedings of the Second International Conference on Very Large
Databases, Mumbai (Bombay), India. San Francisco: Morgan Kaufmann, pp.
544–555.
Shawe-Taylor, J., and N Cristianini. 2004. Kernel methods for pattern analysis.
Cambridge, UK: Cambridge University Press.
Smola, A. J., and B. Schölkopf. 2004. A tutorial on support vector regression.
Statistics and Computing 14(3):199–222.
Soderland, S., D. Fisher, J. Aseltine, and W. Lehnert. 1995. Crystal: inducing a con-
ceptual dictionary. Proceedings of the Fourteenth International Joint Conference
on Artificial Intelligence, Montreal, Canada. Menlo Park, CA: AAAI Press, pp.
1314–1319
Stevens, S. S. 1946. On the theory of scales of measurement. Science 103:677–680.
R E F E R E N C E S
5 0 1
P088407-REF.qxd 4/30/05 11:24 AM Page 501
Dostları ilə paylaş: |