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 desktop studying and comparable ?elds to percentage their new principles and learn ?ndings. This 12 months we got 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. so much submissions bought 4 studies, a couple of submissions bought ?ve studies, whereas purely numerous submissions bought 3 studies. every one submission used to be dealt with via a space Chair who coordinated discussions between reviewers and made suggestion at the submission. this system Committee Chairs tested the reports and meta-reviews to extra warrantly the reliability and integrity of the reviewing procedure. Twenty-nine - pers have been chosen after this method. to make sure that vital revisions required by means of reviewers have been included into the ?nal authorized papers, and to permit submissions which might have - tential after a cautious revision, this yr we introduced a “revision double-check” method. 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 permitted paper was once tested by means of the realm Chair and software 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
A survey on transfer learning. Technical Report HKUST-CS08-08, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China (November 2008) 14. : Constructing informative priors using transfer learning. In: Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, Pennsylvania, USA, June 2006, pp. 713–720 (2006) 15. : Improving svm accuracy by training on auxiliary data sources. In: Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada.
Time is measured in seconds, and memory in MB. SEA W= 50000 Time Acc. Mem. 00 RandomRBF No Drift 50 centers Time Acc. 03 Mem. 60 The size of these datasets is small, compared to tens of millions of training examples of synthetic datasets: 45, 312 for ELEC dataset, 581, 012 for CoverType, and 1, 000, 000 for Poker-Hand. Another important fact is that we do not know when drift occurs or indeed if there is any drift. We may simulate RCD concept drift, joining the three datasets, merging attributes, and supposing that each dataset corresponds to a diﬀerent concept.
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. Electricity Is another widely used dataset described by M. Harries  and analysed by Gama . This data was collected from the Australian New South Wales Electricity Market. In this market, the prices are not ﬁxed and are aﬀected by demand and supply of the market.
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.)