Download Algorithmic Learning Theory: 11th International Conference, by William W. Cohen (auth.), Hiroki Arimura, Sanjay Jain, Arun PDF

By William W. Cohen (auth.), Hiroki Arimura, Sanjay Jain, Arun Sharma (eds.)

This booklet constitutes the refereed court cases of the eleventh foreign convention on Algorithmic studying idea, ALT 2000, held in Sydney, Australia in December 2000.
The 22 revised complete papers awarded including 3 invited papers have been rigorously reviewed and chosen from 39 submissions. The papers are equipped in topical sections on statistical studying, inductive common sense programming, inductive inference, complexity, neural networks and different paradigms, help vector machines.

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Additional resources for Algorithmic Learning Theory: 11th International Conference, ALT 2000 Sydney, Australia, December 11–13, 2000 Proceedings

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In other words, the larger output scale is constraining y(1) = y(2). A similar constraint can also be imposed through the merge techniques shown in Figure 2(c) and (d). These constraints can be made “soft” through Bayesian methods. For example, rather than banning the 01 and 10 values for y , we can just impose a penalty for using them by assigning them lower prior probability. In addition to building a smoothness constraint into the model, we can also impose smoothness by preprocessing the data to smooth the y values prior to running the base learning algorithm.

Lipton, J. F. Naughton, D. A. Schneider, and S. 195–226, 1993. 28, 33 16. R. J. Lipton and J. F. 18–25, 1995. 28, 33 17. J. F. Lynch, Analysis and application of adaptive sampling, in Proc. 260–267, 1999. 28, 33 18. O. Maron and A. Moore, Hoeffding races: accelerating model selection search for classification and function approximation, in Advances in Neural Information Processing Systems, Morgan Kaufmann, 59–66, 1994. 28 19. T. Scheffer and S. Wrobel, A sequential sampling algorithm for a general class of utility criteria, in Proc.

1 Introduction Random sampling is an important technique in computer science for developing efficient randomized algorithms. A task such as estimating the proportion of instances with a certain property in a given data set can often be achieved by randomly sampling a relatively small number of instances. , [9]). In particular, the Chernoff bound and the Hoeffding bound have been used commonly in theoretical computer science because they derive a theoretically guaranteed sample size sufficient for achieving a given task with given accuracy and confidence.

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