New PDF release: Advances in Knowledge Discovery and Data Mining: 16th

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 may possibly 2012. the whole of 20 revised complete papers and sixty six revised brief papers have been conscientiously reviewed and chosen from 241 submissions. The papers current new rules, unique study effects, and functional improvement stories from all KDD-related components. The papers are equipped in topical sections on supervised studying: lively, ensemble, rare-class and on-line; unsupervised studying: clustering, probabilistic modeling within the first quantity and on trend mining: networks, graphs, time-series and outlier detection, and knowledge manipulation: pre-processing and measurement relief within the moment volume.

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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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It means that it is actually worthwhile to issue another query in AC+Q. Another question is whether AC+P introduces noise to the training sets. According to our calculation, the noise rate is at most 5% on all the four datasets. Hence, it is reasonable that AC+Q and AC+P can further improve AC+. However, between AC+Q and AC+P, there is no consistent winner on all the four datasets. On 20 Newsgroup and DMOZ, AC+P achieves higher performance, while on OHSUMED and RCV1, AC+Q is more promising. We also try to make a simple combination of Query and Predict with AC+ (we call it AC+QP ), but the performance is not significantly better than AC+Q and AC+P.

Other strategies, such as expected error reduction [12] and representative sampling [18] can also be used. We will study them in the future. We split all the four datasets into labeled (1%), unlabeled (89%) and testing (10%) parts. As we already know the labels of unlabeled examples, we will use the simulating oracles instead of the real human oracles (experts). 1). The training process is decomposed into a sequence of iterations. In each iteration, each category simultaneously selects a fixed number of examples9 from its local unlabeled pool and queries the oracles (one query will be consumed when we ask one oracle for one label).

To that end, we introduce three adaptive frameworks which allow the summation weights to vary over time - TeamSkill-AllK-Ev-OL1 (OL1), TeamSkill-AllK-EvOL2 (OL2), and TeamSkill-AllK-Ev-OL3 (OL3). 1 TeamSkill-AllK-Ev-OL1 When attempting to construct an overall team skill rating, one key challenge to overcome is the fact that the amount of group history can vary over time. Consider figure 1: after the first game is played, history is available for all possible groups of players. Later, player 4 leaves the team and is replaced by player 5, who has never played with players 1, 2, or 3, leaving only a subset of history available and none for the team as a whole.

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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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