Advances in Machine Learning: First Asian Conference on by Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio
By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on laptop 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 target to supply a number one foreign discussion board for researchers in computing device studying and similar ?elds to proportion their new rules and study ?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. so much submissions acquired 4 studies, a number of submissions obtained ?ve reports, whereas merely numerous submissions bought 3 reports. each one submission used to be 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 reports and meta-reviews to additional warrantly the reliability and integrity of the reviewing technique. Twenty-nine - pers have been chosen after this procedure. to make sure that very important revisions required by way of 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” procedure. briefly, the above-mentioned 29 papers have been conditionally accredited, 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 authorized paper used to be tested by way of the world Chair and application Committee Chairs. Papers that did not cross the exam have been ?nally rejected.
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In: KDD 2008, pp. 812–820. ACM, New York (2008) A Hierarchical Face Recognition Algorithm Remco R. nz Abstract. In this paper, we propose a hierarchical method for face recognition where base classiﬁers are deﬁned to make predictions based on various diﬀerent principles and classiﬁcations are combined into a single prediction. Some features are more relevant to particular face recognition tasks than others. The hierarchical algorithm is ﬂexible in selecting features relevant for the face recognition task at hand.
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After training the RMGM, the missing values in the K given rating matrices can then be generated by (z) (z) fR (ui , vi ) = (k) r (l) (k) (z) (l) (z) P (r|cU , cV )P (cU |ui )P (cV |vi ). r (6) k,l To evaluate the transfer learning framework, we compare our RMGM-based multi-task method to two baseline single-task methods: Pearson correlation coefficients (PCC) and flexible mixture model (FMM). FMM can be viewed as a single-task version of RMGM. In this experiment, we randomly select 500 users and 1000 items from three real-world data sets (MovieLens, EachMovie, and Book-Crossing), respectively.