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 used to be the ?rst variation of a chain of annual meetings which target to supply a number one overseas discussion board for researchers in computing device studying and similar ?elds to percentage their new principles and learn ?ndings. This yr 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 acquired 4 experiences, a couple of submissions acquired ?ve reports, whereas in simple terms numerous submissions bought 3 studies. each one submission was once 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 additional warrantly the reliability and integrity of the reviewing strategy. Twenty-nine - pers have been chosen after this approach. to make sure that very important revisions required through reviewers have been integrated into the ?nal accredited papers, and to permit submissions which might have - tential after a cautious revision, this yr we introduced a “revision double-check” approach. 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 accredited paper was once tested through the world Chair and software Committee Chairs. Papers that didn't cross 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
1 Datasets for Concept Drift Synthetic data has several advantages – it is easier to reproduce and there is little cost in terms of storage and transmission. For this paper we use the data generators most commonly found in the literature. SEA Concepts Generator. This artiﬁcial dataset contains abrupt concept drift, ﬁrst introduced in . It is generated using three attributes, where only the two ﬁrst attributes are relevant. All the attributes have values between 0 and 10. The points of the dataset are divided into 4 blocks with diﬀerent concepts.
We have developed a series of transfer learning algorithms to help learn action models in a new domain. In this section, we summarize our action-model learning algorithm that transfers from a source domain to a target domain. We call the algorithm t-LAMP, which stands for transfer Learning Action Models from Plan traces. As an example, we may have a domain elevator2 where action models are already encoded. f2)’ which means the elevator can go up from a floor ‘f1’ to a floor ‘f2’. f2)’. 2)’ in a new briefcase1 domain, where a case is moved from location ‘l1’ to location ‘l2’.
ACM, New York (2008) 19. : Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996) 20. : Learning drifting concepts: Example selection vs. example weighting. Intell. Data Anal. 8(3), 281–300 (2004) 21. : The problem of concept drift: Definitions and related work 22. : Mining time-changing data streams. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 97–106. ACM Press, New York (2001) 23.