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Volume 41, Number 3, 2002
Artificial Intelligence
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

Cross training and its application to skill mining - References
by D. A. Oblinger, M. Reid, M. Brodie, and R. de Salvo Braz

Cited references

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  2. T. H. Davenport and L. Prusak, Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston, MA (1998).
  3. T. A. Stewart, Intellectual Capital: The New Wealth of Organizations, Doubleday/Currency Publishers, New York (1997).
  4. T. Mitchell, Machine Learning, McGraw-Hill Companies, New York (1997), pp. 201–202.
  5. C. Faloutsos and D. Oard, A Survey of Information Retrieval and Filtering Methods, Technical Report CS-TR3514, Department of Computer Science, University of Maryland, College Park, MD (1995).
  6. C. Manning and H. Schutze, Foundations of Statistical Natural Language Processing, MIT Press, Cambridge, MA (1999).
  7. T. K. Landauer, P. W. Foltz, and D. Laham, “Introduction to Latent Semantic Analysis,” Discourse Processes 25, 259–284 (1998).
  8. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York (2001).
  9. N. Japkowicz, “Learning from Imbalanced Data Sets: A Comparison of Various Strategies,” Papers, AAAI Workshop on Learning from Imbalanced Data Sets, Technical Report WS-00-05, AAAI Press, Menlo Park, CA (2000).
  10. M. Pazzani, C. Merz, P. Murphy, K. Ali, T. Hume, and C. Brunk, “Reducing Misclassification Costs,” Proceedings, Eleventh International Conference on Machine Learning, New Brunswick, NJ (July 10–15, 1994), pp. 217–225.
  11. M. Kubat and S. Matwin, “Addressing the Curse of Imbalanced Data Sets: One-Sided Sampling,” Proceedings, Fourteenth International Conference on Machine Learning, Nashville, TN (July 8–11, 1997), pp. 179–186.
  12. G. M. Weiss and F. Provost, The Effect of Class Distribution on Classifier Learning: An Empirical Study, Technical Report ML-TR-44, Department of Computer Science, Rutgers University, New Brunswick, NJ (2001).
  13. M. Black and R. Hickey, “Maintaining the Performance of a Learned Classifier Under Concept Drift,” Intelligent Data Analysis 3, No. 6, 453–474 (1999).
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