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A bayesian nonparametric estimator based on left censored by Walker S., Muliere P.

By Walker S., Muliere P.

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They are characterized by fast learning with time complexity 0(n) and well-known statistical properties including small variance. -d until a sample size n^ is reached satisfying MSE(n^) < {\+a^)MSE{N). A direct consequence of the progressive sampling results [9] for models with time complexity 0(n) is that the time complexity of this procedure for a=2 is at most twice the time of learning on the whole data set. g. the feedforward neural networks without hidden nodes). 2 Down-Sampling Extension for Slower and Unstable Algorithms Complex nonlinear learning algorithms such as feedforward neural networks with a number of hidden nodes typically have a large variance meaning that their MSE(/3fn}) can largely differ over different weight's initial conditions and choice of training data.

UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California, Irvine, CA, 1999. , Large Datasets Lead to Overly Complex Models: An Explanation and a Solution, Proc. Fourth Int'l Conf. on Knowledge Discovery and Data Mining, 1998. , Efficient Progressive Sampling, Proc. Fifth Int'l Conf. on Knowledge Discovery and Data Mining, 1999. , Introduction to Data Compression, Academic Press/Morgan Kaufmann, 1996. , JAM: Java Agents for Meta-leaming over Distributed Databases, Proc.

Freeman, 1979. 6. S. Matsumoto, Y. Hayashi, and T. Shoudai. Polynomial time inductive inference of regular term tree languages from positive data. Proc. ALT-97, Springer- Verlag, LNAI 1316, pages 212-227, 1997. 7. T. Miyahara, T. Shoudai, T. Uchida, T. Kuboyama, K. Takahashi, and H. Ueda. Discovering new knowledge from graph data using inductive logic programming. Proc. ILP-99, Springer-Verlag, LNAI 1634, pages 222-233, 1999. 8. T. Miyahara, T. Uchida, T. Kuboyama, T. Yamamoto, K. Takahashi, and H.

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