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Please Note: This bibliography material is provided for your personal use only and may not be retransmitted or redistributed without permission in writing from the paper's publisher and/or author. To get copies of these papers, please contact the authors.
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Anand Rangarajan, Haili Chui, Eric Mjolsness, Suguna Pappu, Lila Davachi, Patricia S. Goldman-Rakic, and
James S. Duncan.
A Robust Point Matching Algorithm for Autoradiograph Alignment.
Medical Image Analysis, Vol 1 No 4, 1997. Anand Rangarajan, Alan Yuille, Steven Gold and Eric Mjolsness.
A Convergence Proof for the Softassign Quadratic Assignment Algorithm.
Advances in Neural Information Processing Systems 9. M. Mozer, M. Jordan, and T. Petsche, eds. MIT
Press, 1997. C. E. Brodley and P. Smyth.
Applying classification algorithms in practice.
Statistics and Computing,, 7(1), 1997, pp. 45-56. C. Glymour, D. Madigan, D. Pregibon, and P. Smyth.
Statistical themes and lessons for data mining.
Journal of Data Mining and Knowledge Discovery, 1997. Dennis DeCoste.
Automated learning and monitoring of limit functions.
. Proceedings of the Fourth International Symposium on Artificial Intelligence, Robotics, and Automation for
Space, 1997. Dennis DeCoste.
Mining multivariate time-series sensor data to discover behavior envelopes.
. Proceedings of the Third Conference on Knowledge Discovery and Data Mining, 1997.
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Eric Mjolsness.
Symbolic Neural Networks Derived from Stochastic Grammar Domain Models.
in Connectionist Symbolic Integration, eds. R. Sun and F. Alexandre, Lawrence Erlbaum Associates,
1997.
M. Turmon and J. Pap.
Segmenting chromospheric images with Markov random fields.
In Statistical Challenges in Modern Astronomy II, eds. G. J. Babu and E. D. Feigelson, Springer, 1997,
pp. 409-411.
M. Turmon, S. Mukhtar, and J. Pap.
Bayesian inference for identifying solar active regions.
Proc. Third Conf. on Knowledge Discovery and Data Mining, 1997.
M. Turmon, S. Mukhtar.
Recognizing chromospheric objects via Markov chain Monte Carlo.
Proc. IEEE ICIP, 1997.
M.C. Burl.
Recognition of Visual Object Classes.
Ph.D. Thesis, California Institute of Technology, Dept. of Electrical Engineering, June 1997.
(abstract).
P. Smyth and D. Wolpert.
Stacked density estimation.
UC Irvine Technical Report UCI-ICS 97-36, 1997 (also presented at NIPS 97).
P. Smyth, D. Heckerman, and M. Jordan.
Probabilistic independence networks for hidden Markov models.
Neural Computation, Vol. 9, 1997.
P. Smyth.
Bounds on the mean classification error rate of multiple experts.
Pattern Recognition Letters, 1997.
P. Smyth.
Clustering sequences with hidden Markov models.
In Advances in Neural Information Processing 9, M. C. Mozer, M. I. Jordan, and T. Petsche (eds.), MIT
Press, 1997.
P. Smyth.
Clustering sequences with hidden Markov models.
In Advances in Neural Information Processing 9, M. C. Mozer, M. I. Jordan, and T. Petsche (eds.), MIT
Press, 1997.
Steve Chien, Dennis DeCoste, Richard Doyle, Paul Stolorz.
Making an impact: Artificial intelligence at the Jet Propulsion Laboratory.
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