By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on computing device studying (ACML 2009) used to be held at Nanjing, China in the course of November 2–4, 2009.This used to be the ?rst variation of a chain of annual meetings which objective to supply a number one foreign discussion board for researchers in laptop studying and comparable ?elds to percentage their new rules and learn ?ndings. This 12 months we got 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. such a lot submissions got 4 experiences, a number of submissions obtained ?ve reports, whereas in simple terms a number of submissions bought 3 experiences. each one submission was once dealt with by means of 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 strategy. Twenty-nine - pers have been chosen after this technique. to make sure that very important revisions required via reviewers have been integrated into the ?nal accredited papers, and to permit submissions which might have - tential after a cautious revision, this 12 months we introduced a “revision double-check” technique. briefly, the above-mentioned 29 papers have been conditionally authorized, and the authors have been asked to include the “important-and-must”re- sionssummarizedbyareachairsbasedonreviewers’comments.Therevised?nal model and the revision checklist of every conditionally approved paper was once tested via the world Chair and application Committee Chairs. Papers that did not cross the exam have been ?nally rejected.
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Extra info 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.
Together they form a Markov network, where each formula is a clique with a potential function taking on binary value. With the MLN, we can incorporate the training plan traces and set the learning component to run to complete. This will give a set of final weights, which are indicative of the set of formulas that should be used for the target action model. Thus, in the third step, we merge the action models from the source domain into another MLN, denoted as M ∗ , that include the traces in the target domain.
In the future, we will see many other applications of transfer learning to emerge, and to see more scaled up applications of transfer learning as well. Acknowledgement We thank the support of NEC China Lab. We thank Wenyuan Dai, Guirong Xue, Yuqiang Chen, Xiao Ling, Bin Li, Hankz Hankui Zhuo, Vincent Wenchen Zheng, Sinno Jialin Pan for their great help with this article. References 1. : Learning one more thing. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp.
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.)