By Imre Csiszár (auth.), Jean-François Jean-Fran, Michael R. Berthold, Tamás Horváth (eds.)
This publication constitutes the refereed lawsuits of the eleventh foreign convention on Discovery technology, DS 2008, held in Budapest, Hungary, in October 2008, co-located with the nineteenth foreign convention on Algorithmic studying thought, ALT 2008.
The 26 revised lengthy papers offered including five invited papers have been conscientiously reviewed and chosen from fifty eight submissions. The papers deal with all present matters within the sector of improvement and research of equipment for clever info research, wisdom discovery and laptop studying, in addition to their program to medical wisdom discovery. The papers are geared up in topical sections on studying, function choice, institutions, discovery methods, studying and chemistry, clustering, established info, and textual content analysis.
Read Online or Download Discovery Science: 11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008. Proceedings PDF
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Extra info for Discovery Science: 11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008. Proceedings
Thereafter, we will see three measures that use hPrecision for optimizing consistency, but use different measures (hRecall , hWRA , hCoverage ) for optimizing coverage. Cost Measure hcost = c · p − (1 − c) · n Allows to directly trade off consistency and coverage with a parameter c ∈ [0, 1]. c = 0 only considers consistency, c = 1 only coverage. If c = 1/2, the resulting heuristic (hAccuracy = p − n) is equivalent to accuracy, which computes the percentage of correctly classified examples among all training examples.
With IFGT the Gaussian kernel can be approximated in O nk r(p −1)d + nk time with O t log k + tr(p −1)d time preparation. Here k < t is the number of clusters in the example space and k < k the maximum number of neighbor clusters and p the number of Taylor series terms to be computed. Both the p and k depend on the desired error bound > 0 and k also depends on Gaussian kernel bandwidth. The space usage is O kr(p −1)d + t + n . Recall that rpd = O(dp ). The procedure is interesting in our application, because if we cluster the whole instance space at once, we can reduce the running time signiﬁcantly.
In: Proceedings of the Twenty-Second AAAI Conference on Artiﬁcial Intelligence, pp. 1637–1641. AAAI Press, Menlo Park (2007) Unsupervised Classiﬁer Selection Based on Two-Sample Test 39 19. : A Hilbert space embedding for distributions. , Takimoto, E. ) ALT 2007. LNCS (LNAI), vol. 4754, pp. 13–31. Springer, Heidelberg (2007) 20. : Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics 22(14), 49–57 (2006) 21. : Correcting sample selection bias by unlabeled data.
Discovery Science: 11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008. Proceedings by Imre Csiszár (auth.), Jean-François Jean-Fran, Michael R. Berthold, Tamás Horváth (eds.)