By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on laptop studying (ACML 2009) used to be held at Nanjing, China in the course of November 2–4, 2009.This was once the ?rst version of a sequence of annual meetings which target to supply a number one overseas discussion board for researchers in desktop studying and similar ?elds to proportion their new rules and learn ?ndings. This yr we acquired 113 submissions from 18 international locations and areas in Asia, Australasia, Europe and North the US. The submissions went via a r- orous double-blind reviewing method. such a lot submissions got 4 stories, a couple of submissions acquired ?ve stories, whereas in simple terms numerous 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 extra warrantly the reliability and integrity of the reviewing method. Twenty-nine - pers have been chosen after this procedure. to make sure that very important revisions required via reviewers have been integrated 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” approach. briefly, 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 checklist of every conditionally approved paper used to be tested through the world Chair and application Committee Chairs. Papers that didn't move 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
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36 A. Bifet et al. The new improved ensemble methods presented in this paper are slower than the old ones, with BagADWIN 10 HAT being worst, because it pays twice: through the addition of ADWIN and HAT, the latter being slower than HT by a factor between two and ﬁve. Change detection can be time-consuming, the extreme case being Naive Bayes vs. NBAdwin, where Naive Bayes can be up to six times faster. However, change detection helps accuracy, with improvements of up to 20 percentage points (see, for example, CovPokElec).