By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on desktop 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 target to supply a number one foreign discussion board for researchers in laptop studying and similar ?elds to proportion their new principles and examine ?ndings. This yr we obtained 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 procedure. such a lot submissions bought 4 experiences, a couple of submissions obtained ?ve experiences, whereas in basic terms numerous submissions obtained 3 stories. each one submission was once 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 reports and meta-reviews to additional warrantly the reliability and integrity of the reviewing approach. Twenty-nine - pers have been chosen after this method. to make sure that vital revisions required through 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” technique. 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 checklist of every conditionally authorised paper used to be tested by means of the realm Chair and software Committee Chairs. Papers that didn't go 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
Comparison of algorithms. Accuracy is measured as the ﬁnal percentage of examples correctly classiﬁed over the 1 or 10 million test/train interleaved evaluation. Time is measured in seconds, and memory in MB. 001 50 centers 50 centers Time Acc. Mem. Time Acc. Mem. 36 model before using it to train. This interleaved test followed by train procedure was carried out on 10 million examples from the hyperplane and RandomRBF datasets, and one million examples from the SEA dataset. Tables 1, 2 and 3 reports the ﬁnal accuracy, and speed of the classiﬁcation models induced on synthetic data.
As described earlier, we implemented the following new variants of bagging: – ADWIN Bagging using Hoeﬀding Adaptive Trees. – Bagging ASHT using the DDM drift detection method – Bagging ASHT using the EDDM drift detection method Improving Adaptive Bagging Methods for Evolving Data Streams 35 Table 4. Comparison of algorithms on real data sets. Time is measured in seconds, and memory in MB. The results are based on a single run. Unlike synthetic datasets, it is not straightforward to add randomization to real datasets such that meaningful datasets and analysis of performance variances are obtained.
Bifet et al. Table 3. Comparison of algorithms. Accuracy is measured as the ﬁnal percentage of examples correctly classiﬁed over the 1 or 10 million test/train interleaved evaluation. Time is measured in seconds, and memory in MB. 001 50 centers 50 centers Time Acc. Mem. Time Acc. Mem. 36 model before using it to train. This interleaved test followed by train procedure was carried out on 10 million examples from the hyperplane and RandomRBF datasets, and one million examples from the SEA dataset.