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By Ryan Rossi, Jennifer Neville (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)

ISBN-10: 3642302165

ISBN-13: 9783642302169

ISBN-10: 3642302173

ISBN-13: 9783642302176

The two-volume set LNAI 7301 and 7302 constitutes the refereed complaints of the sixteenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2012, held in Kuala Lumpur, Malaysia, in might 2012. the whole of 20 revised complete papers and sixty six revised brief papers have been rigorously reviewed and chosen from 241 submissions. The papers current new rules, unique study effects, and functional improvement reviews from all KDD-related parts. The papers are geared up in topical sections on supervised studying: lively, ensemble, rare-class and on-line; unsupervised studying: clustering, probabilistic modeling within the first quantity and on development mining: networks, graphs, time-series and outlier detection, and knowledge manipulation: pre-processing and size relief within the moment volume.

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Read Online or Download Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June 1, 2012, Proceedings, Part I PDF

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Additional info for Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June 1, 2012, Proceedings, Part I

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2 Related Work The foundations of modern skill assessment approaches date back to the work of Louis Leon Thurstone [10] who, in 1927, proposed the “law of comparitive judgement”, a method by which the mean distance between two physical stimuli, such as perceived loudness, can be computed in terms of the standard deviation when the stimuli processes are normally-distributed. In 1952, Bradley-TerryLuce (BTL) models [11] introduced a logistic variant of Thurstone’s model, using taste preference measurements for evaluation.

The typical active learning steps are numbered 1, 2, 3 in the figure. 1 Unlabeled Pool Building Policy From step one of our algorithm, we know that the training examples for a deep category (say c) must belong to its ancestor categories. However, it is likely that many unlabeled examples do not belong to the ancestor categories of c. We define those examples as out-of-scope examples. If those out-of-scope examples are selected by c, we may waste a lot of queries. Thus, instead of using one shared unlabeled pool [5] for all categories, we construct a local unlabeled pool on each of the categories.

TKDD 5(2), 10 (2011) 8. : Across-model collective ensemble classification. AAAI (2011) ˙ C ¨ gu 9. : GA-TVRC: A Novel Relational Time Varying Classifier to Extract Temporal Information Using Genetic Algorithms. In: Perner, P. ) MLDM 2011. LNCS, vol. 6871, pp. 568–583. Springer, Heidelberg (2011) 10. : Structure prediction in temporal networks using frequent subgraphs. In: CIDM, pp. 35–42 (2007) 11. : Spatiotemporal Relational Probability Trees: An Introduction. In: ICDM, pp. 935–940 (2008) 12. : Learning relational probability trees.

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Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June 1, 2012, Proceedings, Part I by Ryan Rossi, Jennifer Neville (auth.), Pang-Ning Tan, Sanjay Chawla, Chin Kuan Ho, James Bailey (eds.)


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