By Ana Fred, Maria De Marsico, Antoine Tabbone
ISBN-10: 3319255290
ISBN-13: 9783319255293
ISBN-10: 3319255304
ISBN-13: 9783319255309
This publication constitutes the completely refereed post-conference lawsuits of the 3rd foreign convention on development acceptance, ICPRAM 2014, held in Angers, France, in March 2014.
The 18 revised complete papers have been rigorously reviewed and chosen from 179 submissions and describe up to date functions of development acceptance innovations to real-world difficulties, interdisciplinary examine, experimental and/or theoretical stories yielding new insights that strengthen development reputation methods.
Read or Download Pattern Recognition Applications and Methods: Third International Conference, ICPRAM 2014, Angers, France, March 6-8, 2014, Revised Selected Papers PDF
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Extra info for Pattern Recognition Applications and Methods: Third International Conference, ICPRAM 2014, Angers, France, March 6-8, 2014, Revised Selected Papers
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References 1. : Bagging predictors. Mach. Learn. 24, 123–140 (1996) 2. : Random forests. Mach. Learn. 45, 5–32 (2001) 3. : Biclustering in data mining. Comput. Oper. Res. 35(9), 2964–2987 (2008) 4. : Biclustering of expression data. In: Proceedings of the International Conference Intelligent Systems for Molecular Biology, vol. 8, pp. 93– 103 (2000) 5. : An ensemble biclustering approach for querying gene expression compendia with experimental lists. Bioinformatics 27(14), 1948–1956 (2011) 6. : Maximum likelihood from incomplete data via the em algorithm.
This procedure consists in finding the submatrix containing the maximum of points that are in the aggregated bicluster and the minimum of points that are not in the aggregated bicluster. The k-th aggregated bicluster is reconstructed by minimizing the following function: N M δ(ei ∈ E Bk ∧ fi ∈ F Bk )δ(I ij = k) L(Bk ) = i=1 j=1 + δ(ei ∈ / E Bk ∨ fi ∈ / F Bk )δ(I ij = k). This optimization problem is solved by a heuristic procedure. We started with all the examples and features involved in the aggregated bicluster.
Nonlinear Estimation and Classification, vol. 171, pp. 149–171. Springer, New York (2003) 26. : Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002) 27. : Analysis of consensus partition in cluster ensemble. In: Fourth IEEE International Conference on Data Mining, pp. 225–232 (2004) 28. : A mixture model of clustering ensembles. In: Proceedings of the SIAM International Conference on Data Mining (2004) 29. : Improved biclustering of microarray data demonstrated through systematic performance tests.
Pattern Recognition Applications and Methods: Third International Conference, ICPRAM 2014, Angers, France, March 6-8, 2014, Revised Selected Papers by Ana Fred, Maria De Marsico, Antoine Tabbone
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