By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on computing device studying (ACML 2009) used to be held at Nanjing, China in the course of November 2–4, 2009.This was once the ?rst variation of a sequence of annual meetings which target to supply a number one overseas discussion board for researchers in computer studying and similar ?elds to proportion their new principles and learn ?ndings. This 12 months we obtained 113 submissions from 18 international locations and areas in Asia, Australasia, Europe and North the USA. The submissions went via a r- orous double-blind reviewing approach. such a lot submissions got 4 experiences, a couple of submissions obtained ?ve experiences, whereas basically a number of submissions bought 3 experiences. every one submission was once 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 experiences and meta-reviews to additional warrantly the reliability and integrity of the reviewing method. Twenty-nine - pers have been chosen after this procedure. to make sure that vital revisions required by way of reviewers have been integrated into the ?nal permitted 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 used to be tested by way of the realm Chair and software Committee Chairs. Papers that didn't go 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
In this paper, we explore various features based on outline recognition, PCA classiﬁers applied to part of the face and exploitation of symmetry in faces. 25% accuracy on the ATT dataset) at reduced computation cost compared to full PCA. 1 Introduction Many face recognition algorithms rely on general purpose techniques like principle component analysis (PCA) , linear discriminant analysis (LDA)  or independent component analysis (ICA) [4,8]1 . These algorithms are general purpose image recognition algorithms that do not use background knowledge of the problem at hand, but transform the space represented by an image into another space where each of the dimensions spanning the space represent otherwise unknown informative features of the pictures of faces.
Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeﬀding Tree (ASHT) Bagging. ASHT Bagging uses trees of diﬀerent sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeﬀding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeﬀding Tree (ASHT) Bagging, we add an error change detector for each classiﬁer.
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