By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)

ISBN-10: 364205224X

ISBN-13: 9783642052248

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 goal to supply a number one overseas discussion board for researchers in desktop studying and comparable ?elds to proportion their new rules and study ?ndings. This 12 months we bought 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 technique. so much submissions acquired 4 stories, a number of submissions acquired ?ve stories, whereas in basic terms numerous submissions acquired 3 studies. every one submission used to be dealt with through a space Chair who coordinated discussions between reviewers and made advice at the submission. this system Committee Chairs tested the studies and meta-reviews to additional warrantly the reliability and integrity of the reviewing approach. Twenty-nine - pers have been chosen after this approach. to make sure that vital revisions required by means of reviewers have been included 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 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 accredited paper was once tested by way of the world Chair and application Committee Chairs. Papers that didn't go the exam have been ?nally rejected.

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Extra resources for Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings

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Can chinese web pages be classified with english data source? In: WWW 2008: Proceeding of the 17th International conference on World Wide Web, pp. 969–978. ACM, New York (2008) 29. : Transferring knowledge from another domain for learning action models. -H. ) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 1110–1115. Springer, Heidelberg (2008) 30. : Transfer learning for collaborative filtering via a rating-matrix generative model. In: ICML, pp. 617–624 (2009) 31. : Estimating location using Wi-Fi.

Transferring naive bayes classifiers for text classification. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (July 2007) 9. : Domain adaptation with structural correspondence learning. In: Proceedings of the Conference on Empirical Methods in Natural Language, Sydney, Australia, pp. 120–128 (2006) Transfer Learning beyond Text Classification 21 10. : Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, pp.

The results for the Electricity dataset were structurally similar to those for the Forest CoverType dataset and therefore not reported. Additionally, the learning curves and model growth curves for LED dataset are plotted (Figure 3). The first, and baseline, algorithm (HT) is a single Hoeffding tree, enhanced with adaptive Naive Bayes leaf predictions. 05, as used in [10]. 2. Bag10 is Oza and Russell online bagging using ten classifiers and Bag5 only five. 2. As described earlier, we implemented the following new variants of bagging: – ADWIN Bagging using Hoeffding Adaptive Trees.

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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.)


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