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Bibliography

1
M. K. Aguilera, J. C. Mogul, J. L. Wiener, P. Reynolds, and A. Muthitacharoen.
Performance debugging for distributed systems of black boxes.
In Proc. 19th ACM Symposium on Operating Systems Principles, Bolton Landing, NY, 2003.

2
P. Barham, R. Isaacs, and R. Mortier.
Using magpie for request extraction and workload modeling.
In 6th Symposium on Operating Systems Design and Implementation (OSDI'04), Dec. 2004.

3
Bayesian network classifier toolbox.
https://jbnc.sourceforge.net/.

4
J. Binder, D. Koller, S. J. Russell, and K. Kanazawa.
Adaptive probabilistic networks with hidden variables.
Machine Learning, 29(2-3):213-244, 1997.

5
C. Bishop.
Neural Networks for Pattern Recognition.
Oxford, 1995.

6
C. Blake and C. Merz.
UCI repository of machine learning databases, 1998.

7
P. Bodík, G. Friedman, L. Biewald, H. Levine, G. Candea, A. Fox, M. I. Jordan, D. Patterson, K. Patel, G. Tolle, and J. Hui.
Combining visualization and statistical analysis to improve operator confidence and efficiency for failure detection and localization.
Submitted for publication.

8
G. Candea, S. Kawamoto, Y. Fujiki, G. Friedman, and A. Fox.
A microrebootable system - design, implementation, and evaluation.
In Proc. 6th USENIX Symposium on Operating Systems Design and Implementation, San Francisco, Dec. 2004.

9
N. Cesa-Bianchi, Y. Freund, D. P. Helmbold, and M. Warmuth.
On-line prediction and conversion strategies.
In Computational Learning Theory: Eurocolt '93, pages 205-216. Oxford University Press, 1993.

10
M. Chen, A. Zheng, J. Lloyd, M. Jordan, and E. Brewer.
Failure diagnosis using decision trees.
In First International Conference on Autonomic Computing, New York, NY, May 2004.

11
T. Chou.
The End of Software.
Sams Publishing, Indianapolis, IN, 2005.

12
I. Cohen, M. Goldszmidt, T. Kelly, J. Symons, and J. Chase.
Correlating instrumentation data to system states: A building block for automated diagnosis and control.
In 6th Symposium on Operating Systems Design and Implementation (OSDI'04), Dec. 2004.

13
D. A. Cohn, Z. Ghahramani, and M. I. Jordan.
Active learning with statistical models.
In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems, volume 7, pages 705-712. The MIT Press, 1995.

14
N. Cristianini and J. Shawe-Taylor.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.
Cambridge University Press, March 2000.

15
M. DeGroot.
Optimal Statistical Decisions.
McGraw-Hill, 1970.

16
R. Duda and P. Hart.
Pattern Classification and Scene Analysis.
John Wiley and Sons, New York, 1973.

17
A. Fox and D. Patterson.
Self-repairing computers.
Scientific American, June 2003.

18
A. Fox, D. A. Patterson, and M. I. Jordan.
Reliable adaptive distributed systems (research proposal).
National Science Foundation Award, June 2005.

19
N. Friedman, D. Geiger, and M. Goldszmidt.
Bayesian network classifiers.
Machine Learning, 29:131-163, 1997.

20
N. Friedman and M. Goldszmidt.
Sequential update of Bayesian network structure.
In Proceedings of the International Conference on Uncertainty in Artificial Intelligence, pages 165-174, 1997.

21
N. Friedman and Z. Yakhini.
On the sample complexity of learning Bayesian network.
In Proceedings of the International Conference on Uncertainty in Artificial Intelligence, 1996.

22
T. Hastie, R. Tibshirani, and J. Friedman.
The elements of statistical learning.
Springer, 2001.

23
D. Heckerman, D. Geiger, and D. Chickering.
Learning Bayesian networks: The combination of knowledge and statistical data.
Machine Learning, 20:197-243, 1995.

24
R. Ihaka and R. Gentleman.
R: A language for data analysis and graphics.
Journal of Computational and Graphical Statistics, 5(3):299-314, 1996.

25
M. Kearns and U. Vazirani.
An Introduction to Computational Learning Theory.
MIT press, 1994.

26
J. O. Kephart and D. M. Chess.
The vision of autonomic computing.
Computer, 36(1):41-50, 2003.

27
E. Kiciman and A. Fox.
Detecting application-level failures in component-based internet services.
Submitted for publication, September 2004.

28
R. Kohavi.
A study of cross-validation and bootstrap for accuracy estimation and model selection.
In International Joint Conference on Artificial Intelligence (IJCAI), pages 1137-1145, 1995.

29
T. Lane and C. E. Brodley.
Approaches to online learning and concept drift for user identification in computer security.
In International Conference on Knowledge Discovery and Data Mining, pages 259-263, 1998.

30
D. MacKay.
Information-based objective functions for active data selection.
Neural Computation, 4(4):590-604, 1992.

31
T. Mitchell.
The role of unlabeled data in supervised learning.
In Proc. of International Colloquium on Cognitive Science, 1999.

32
J. Neter, M. Kutner, C. Nachtshein, and W. Wasserman.
Applied Linear Statistical Models.
McGraw-Hill, 1996.

33
K. Nigam, A. McCallum, S. Thrun, and T. Mitchell.
Text classification from labeled and unlabeled documents using EM.
Machine Learning, 39:103-134, 2000.

34
J. Pearl.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
Morgan Kaufmann, 1988.

35
J. Quinlan.
C4.5 Programs for machine learning.
Morgan Kaufmann, 1993.

36
J. Reason.
Managing the Risks of Organizational Accidents.
Ashgate Publishing Co., 1997.

37
J. Redstone, M. Swift, and B. Bershad.
Using computers to diagnose computer problems.
In Proc. 9th Workshop on Hot Topics in Operating Systems, Lihue, Hawaii, June 2003.

38
D. Sullivan.
Using probabilistic reasoning to automate software tuning.
PhD thesis, Harvard University, 2003.

39
S. Tong and D. Koller.
Support vector machine active learning with applications to text classification.
In Proceedings of ICML-00, 17th International Conference on Machine Learning, pages 999-1006, 2000.

40
I. Witten and E. Frank.
Data Mining: Practical machine learning tools with Java implementations.
Morgan Kaufmann, 2000.

41
S. Zhang, I. Cohen, M. Goldszmidt, J. Symons, and A. Fox.
Ensembles of models for automated diagnosis of system performance problems.
In 2005 Intl. Conf. on Dependable Systems and Networks (DSN 2005), Yokohama, Japan, June 2005.



Armando Fox 2005-07-26