By Buntine W., Grobelnik M., Mladenic D., Shawe-Taylor J. (eds.)
This booklet constitutes the refereed complaints of the joint convention on laptop studying and data Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers awarded in volumes, including five invited talks, have been conscientiously reviewed and chosen from 422 paper submissions. as well as the standard papers the quantity comprises 14 abstracts of papers showing in complete model within the computer studying magazine and the information Discovery and Databases magazine of Springer. The convention intends to supply a world discussion board for the dialogue of the most recent top of the range learn leads to all components concerning computer studying and information discovery in databases. the subjects addressed are program of computing device studying and information mining how you can real-world difficulties, quite exploratory examine that describes novel studying and mining initiatives and functions requiring non-standard options
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In: SIAM Int. Conf. on Data Mining (April 2004) 6. : Power laws for monkeys typing randomly: the case of unequal probabilities. IEEE Transactions on Information Theory 50(7), 1403– 1414 (2004) 7. : Self-similarity in world wide web traffic, evidence and possible causes. Sigmetrics, 160–169 (1996) 8. : On the evolution of random graphs. Publ. Math. Inst. Hungary. Acad. Sci. 5, 17–61 (1960) 9. : A network formation game for bipartite exchange economies. In: SODA (2007) 10. : On a network creation game.
Reference 1. : Learning Multi-linear Representations of Distributions for Efficient Inference. 1007/s10994-009-5130-X This is an extended abstract of an article published in the machine learning journal [1]. W. Buntine et al. ): ECML PKDD 2009, Part I, LNAI 5781, p. 11, 2009. es Abstract. This paper analyzes the application of a particular class of Bregman divergences to design cost-sensitive classifiers for multiclass problems. We show that these divergence measures can be used to estimate posterior probabilities with maximal accuracy for the probability values that are close to the decision boundaries.
C Springer-Verlag Berlin Heidelberg 2009 Sparse Kernel SVMs via Cutting-Plane Training Thorsten Joachims and Chun-Nam John Yu Cornell University, Dept. edu While Support Vector Machines (SVMs) with kernels offer great flexibility and prediction performance on many application problems, their practical use is often hindered by the following two problems. Both problems can be traced back to the number of Support Vectors (SVs), which is known to generally grow linearly with the data set size [1]. First, training is slower than other methods and linear SVMs, where recent advances in training algorithms vastly improved training time.