By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on desktop studying (ACML 2009) used to be held at Nanjing, China in the course of November 2–4, 2009.This used to be the ?rst version of a chain of annual meetings which objective to supply a number one overseas discussion board for researchers in computing device studying and similar ?elds to percentage their new principles and study ?ndings. This yr we obtained 113 submissions from 18 nations and areas in Asia, Australasia, Europe and North the United States. The submissions went via a r- orous double-blind reviewing method. so much submissions acquired 4 stories, a number of submissions bought ?ve stories, whereas merely a number of submissions acquired 3 reports. every one submission was once dealt with via a space Chair who coordinated discussions between reviewers and made suggestion at the submission. this system Committee Chairs tested the stories and meta-reviews to additional warrantly the reliability and integrity of the reviewing approach. Twenty-nine - pers have been chosen after this approach. to make sure that vital revisions required by way of reviewers have been integrated into the ?nal permitted papers, and to permit submissions which might have - tential after a cautious revision, this yr we introduced a “revision double-check” procedure. in brief, the above-mentioned 29 papers have been conditionally approved, and the authors have been asked to include the “important-and-must”re- sionssummarizedbyareachairsbasedonreviewers’comments.Therevised?nal model and the revision record of every conditionally authorised paper used to be tested through the world Chair and application Committee Chairs. Papers that didn't cross the exam have been ?nally rejected.
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Additional resources for Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings
Given# means the number of observed ratings for each user. These results are quoted from . 581 20 Q. Yang validated that RMGM indeed can gain additional useful knowledge by pooling the rating data from multiple related domains to make these tasks benefit from one another. 4 Conclusion and Future Work Transfer learning has been proposed as a new learning problem in machine learning, but in data mining community it is still considered a new problem. One of the reasons is that data mining emphasizes scaled up applications to real world problems, and such diverse applications have been rare in transfer learning.
In: KDD 2009. : Classiﬁcation and Regression Trees. : Fast and light boosting for adaptive mining of data streams. , Zhang, C. ) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 282–292. : Improving the performance of an incremental algorithm driven by error margins. Intell. Data Anal. : Mining high-speed data streams. In: Knowledge Discovery and Data Mining, pp. : Learning with drift detection. In: SBIA Brazilian Symposium on Artiﬁcial Intelligence, pp. : Forest trees for on-line data. In: SAC 2004: Proceedings of the 2004 ACM symposium on Applied computing, pp.
10. 000 Instances Fig. 3. Accuracy, runtime and memory on dataset LED with three concept drifts 34 A. Bifet et al. Table 3. Comparison of algorithms. Accuracy is measured as the ﬁnal percentage of examples correctly classiﬁed over the 1 or 10 million test/train interleaved evaluation. Time is measured in seconds, and memory in MB. 001 50 centers 50 centers Time Acc. Mem. Time Acc. Mem. 36 model before using it to train. This interleaved test followed by train procedure was carried out on 10 million examples from the hyperplane and RandomRBF datasets, and one million examples from the SEA dataset.
Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings by Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)