Dragos Margineantu (2001):
Methods for Cost-Sensitive Learning, Oregon State University,
Department of Computer Science (Technical Report). Postscript preprint.
Stefanie Moret, William
Langford, Dragos Margineantu (2006):
Learning to Predict Channel Stability using Biogeomorphic Features,
Ecological Modelling, Vol. 191, Issue 1, Jan. 2006, pp. 47-57, Elsevier. Dragos Margineantu (2005):
Active Cost-Sensitive Learning, Proceedings of the Nineteenth International
Joint Conference on Artificial Intelligence, IJCAI-05. Dragos Margineantu, Michael
Drumheller, Roman Fresnedo (2005): Testing Decision Systems
with Classification Components, Proceedings of the
International Joint Conference on Neural Networks, IJCNN-05. Dragos Margineantu (2004):
Confidence-based Cost-Sensitive Classification Decisions, Proceedings of the 33rd Symposium
on the Interface of Computing Science and Statistics, Baltimore,
MD. Dragos Margineantu (2002):
Class Probability Estimation and Cost-Sensitive Classification
Decisions, Machine Learning: ECML 2002, Proceedings of the 13th
European Conference on Machine Learning, pp.270-281, ©Springer Verlag, Lecture
Notes in Artificial Intelligence, 2430. Postscript preprint.
Dragos Margineantu, Thomas G. Dietterich (2002):
Improved Class Probability
Estimates from Decision Tree Models, in "Nonlinear Estimation and
Classification", D.D. Denison, C.C. Holmes, M.H. Hansen, B. Mallick,
and B. Yu (Eds.), ©Springer Verlag, Lecture Notes in Statistics 171.
Postscript preprint. Dragos Margineantu, Thomas G. Dietterich
(2001):
Lazy Class Probability Estimators,
Proceedings of the 33rd Symposium
on the Interface of Computing Science and Statistics, Costa Mesa, CA. Dragos Margineantu, Thomas G.
Dietterich (2000): Bootstrap Methods for the Cost-Sensitive
Evaluation of Classifiers, Proceedings of the Seventeenth
International Conference on Machine Learning (ICML-2000), pp.583-590,
Morgan Kaufmann, San Francisco, CA. Postscript preprint. Dragos Margineantu (1999):
Building Ensembles of Classifiers for Loss Minimization,
Proceedings of the 31st Symposium on the Interface: Models, Prediction,
and Computing, pp.190-194. Postscript preprint. Dragos Margineantu (1999):
Applying Supervised Learning to Real-World Problems, Proceedings of the Sixteenth National Confernce on Artificial Intelligence (AAAI-99), pp.951. Presented at the SIGART/AAAI-99 Doctoral Consortium. Dragos Margineantu, Thomas G.
Dietterich (1997): Pruning Adaptive Boosting, Proceedings of
the Fourteenth International Conference on Machine Learning (ICML-97),
pp.211-218, Morgan Kaufmann, San Francisco, CA. Postscript preprint. Daniela Crivianu-Gaita, Florin Miclea,
Andrei Gaspar, Dragos Margineantu, and Stefan Holban (1997): 3D reconstruction of prostate from ultrasound images,
International
Journal of Medical Informatics, Vol.45, June 1997, pp.43-51,
Elsevier Science. Dragos Margineantu (1997): Learning by using dynamic feature combination and selection,
Proceedings of the IASTED/AAAI International Conference on Artificial Intelligence and Soft Computing, pp.154-156, ACTA-IASTED Press, Anaheim, CA.
Postscript preprint.
Stephane Chatre, Charles Knutson,
Dragos Margineantu, Carsten Schulz-Key (1996): Improving the DLX
Performance by Taking Some of the Reduction out of RISC, Proceedings
of the International Conference on Technical Informatics (ConTI-96),
Timisoara, Romania. Postscript
preprint. Florin Miclea, Stefan Holban,
Andrei Gaspar, Daniela Crivianu-Gaita, Dragos Margineantu (1995):
Modeling and Volume Determination of the Prostate, Proceedings of
the Symposium on Automatic Control and Computer Science (SACCS-95), Iasi,
Romania. Postscript
preprint.
Dragos Margineantu (2001):
Methods for Cost-Sensitive Learning, Oregon State University,
Department of Computer Science (Technical Report). Postscript preprint.
Dragos Margineantu (2003):
Active Cost-Sensitive Learning The Learning Workshop, 2003, Snowbird, UT.
Dragos Margineantu (2000):
On Class Probability Estimates and Cost-Sensitive Evaluation of
Classifiers, Workshop on Cost-Sensitive Learning, The Seventeenth
International Conference on Machine Learning (ICML-2000).
Dragos Margineantu (2000):
When Does Imbalanced Data Require more than Cost-Sensitive Learning?,
Workshop on Learning from Imbalanced Data, National Conference on Artificial Intelligence (AAAI-2000).
Dragos Margineantu, Thomas G.
Dietterich (1999): Learning Decision Trees for Loss Minimization in
Multi-Class Problems, Technical Report 99-30-03, Department of
Computer Science, Oregon State University.
Dragos Margineantu, Julianne Monell
(1996): The equivalence of Post systems and Turing machines,
TR, Oregon State University.
Dragos Margineantu (2001):
Learning Ensembles for Probability Estimation and Ranking, the
Annual Conference of the Institute for Operations Research and
Management Sciences (INFORMS), November 2001.
Dragos Margineantu (1998): Issues in Applying Divide-and-Conquer Methods for Learning Real-World Problems, Neural Information Processing Systems 1998 (NIPS-98), workshop on "Learning from Ambiguous and Complex Examples".