By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on computing device studying (ACML 2009) was once held at Nanjing, China in the course of November 2–4, 2009.This was once 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 proportion their new rules and examine ?ndings. This 12 months we acquired 113 submissions from 18 international locations and areas in Asia, Australasia, Europe and North the USA. The submissions went via a r- orous double-blind reviewing strategy. so much submissions obtained 4 experiences, a number of submissions got ?ve experiences, whereas merely numerous submissions obtained 3 stories. each one submission was once dealt with via a space Chair who coordinated discussions between reviewers and made advice at the submission. this system Committee Chairs tested the studies and meta-reviews to extra 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 included into the ?nal authorized papers, and to permit submissions which might have - tential after a cautious revision, this 12 months we introduced a “revision double-check” procedure. in brief, the above-mentioned 29 papers have been conditionally authorised, 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 authorized paper used to be tested through the realm Chair and software Committee Chairs. Papers that didn't 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
It uses one instance of ADWIN in each node, as a change detector, to monitor the classiﬁcation error rate at that node. A signiﬁcant increase in that rate indicates that the data is changing with respect Improving Adaptive Bagging Methods for Evolving Data Streams 27 to the time at which the subtree was created. This was the approach used by Gama et al. in , using another change detector. When any instance of ADWIN at a node detects change, we create a new alternate tree without splitting any attribute.
It contains 581, 012 instances and 54 attributes, and it has been used in several papers on data stream classiﬁcation [13, 21]. Poker-Hand Consists of 1, 000, 000 instances and 11 attributes. Each record of the Poker-Hand dataset is an example of a hand consisting of ﬁve playing cards drawn from a standard deck of 52. Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the “Poker Hand”. The order of cards is important, which is why there are 480 possible Royal Flush hands instead of 4.
4 Transfer Learning for Collaborative Filtering Collaborative filtering (CF) is widely used in many real-world recommender systems. A particularly serious problem is that users of CF system can only rate a very limited number of items. As a result, the rating matrices are usually too sparse to model well. This is particularly true for a new rating matrix in a new domain. As an example, when we open a new online service, the rating matrices are often very sparse, which gives rise to the so-called cold-start problem.
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.)