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bookmarks

publications

(649)   

• Text categorization with support vector machines
  T. Joachims Proceedings of the European Conference on Machine Learning, (1998)
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• Attribute selection in neural networks used to classify remotely sensed data
  L. Milne Proc. Visual Information Processing Workshop, Sydney, (1997)
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• An alternative method of cross validation for the smoothing of density estimates
  R. Carroll and P. Hall Journal of the American Statistical Association(1988)
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• Maximum smoothed likelihood density estimation for Inverse Problems
  P. Eggermont and V. LaRicca Annals of Statistics(1995)
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• From image deblurring to optimal investments: Maximum likelihood solutions for positive linear inverse problems
  Y. Vardi and D. Lee Journal of the Royal Statistical Society B(1993)
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• Nonparametric maximum likelihood estimation of a mixing distribution
  N. Laird Journal of the American Statistical Association(1978)
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• A consistent nonparametric density estimator for the deconvolution problem
  M. Liu and R. Taylor Canadian Journal of Statistics(1989)
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• Nonparametric estimation of a mixing density via the kernel method
  C. Goutis Journal of the American Statistical Association92(440):1445-1450(1997)
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• Machine Learning
  T. Mitchell McGraw Hill, (1997)
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• Learning binary relations and total orders
  S. Goldmann and R. Rivest and R. Schapire SIAM Journal of Computing22(5):1006-1034(1993)
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• Self-directed learning
  S. Ben-David and N. Eiron Machine Learning(1998)
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• Mistake Bounds and Logarithmic Linear Threshoild Algorithms
  N. Littlestone U.C. Santa Cruz, (1989)
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• Online Learning versus Offline Learning
  Shai Ben-David and Eyal Kushilevitz and Yishay Mansour Machine Learning(1997)
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• Learning when irrelevant attributes abound: A new linear threshold algorithm
  N. Littlestone Machine Learning(1988)
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• A decision-theoretic generalization of on-line learning and an application to boosting
  Y. Freund and R. Schapire Journal of Computer and System Sciences55(1):119-139(1997)
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• Systems That Learn
  S. Jain and D. Osherson and J. Royer and A. Sharma and S. Weinstein MIT Press, (1999)
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• Introduction to Machine Learning
  N. Nilsson MIT Press (to apprear), (1998)
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• Machine Learning
  B. Natarajan Springer, (1991)
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• Supervised and unsupervised discretization of continuous features
  J. Dougherty and R. Kohavi and M Sahami ICML-95, (1995)
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• Stacked Regressions
  Leo Breiman Machine Learning(1996)
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