By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on computer studying (ACML 2009) was once 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 laptop studying and similar ?elds to percentage their new rules and learn ?ndings. This 12 months 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 got 4 stories, a couple of submissions got ?ve studies, whereas in simple terms numerous submissions bought 3 reports. each one submission used to be dealt with by way of a space Chair who coordinated discussions between reviewers and made suggestion at the submission. this system Committee Chairs tested the studies and meta-reviews to extra warrantly the reliability and integrity of the reviewing method. Twenty-nine - pers have been chosen after this strategy. to make sure that very important revisions required by way of reviewers have been integrated into the ?nal authorised papers, and to permit submissions which might have - tential after a cautious revision, this 12 months we introduced a “revision double-check” approach. in brief, the above-mentioned 29 papers have been conditionally permitted, 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 approved paper used to be tested via the realm Chair and software Committee Chairs. Papers that didn't go the exam have been ?nally rejected.
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Additional info for Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings
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. 632–636. : Accurate decision trees for mining high-speed data streams. In: KDD 2003, August 2003, pp. : Adaptive Filtering and Change Detection. : Splice-2 comparative evaluation: Electricity pricing. : Mining time-changing data streams. In: KDD 2001, San Francisco, CA, pp. 97–106. : Pruning adaptive boosting. In: ICML 1997, pp.
We use two drift detection methods (DDM and EDDM) proposed by Gama et al.  and Baena-Garc´ıa et al. . These methods control the number of errors produced by the learning model during prediction. They compare the statistics of two windows: the ﬁrst contains all of the data, and the second contains only the data from the beginning until the number of errors increases. Their methods do not store these windows in memory. They keep only statistics and a window of recent errors data. The drift detection method (DDM) uses the number of errors in a sample of n examples, modelled by a binomial distribution.
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