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 version of a sequence of annual meetings which target to supply a number one overseas discussion board for researchers in computing device studying and similar ?elds to proportion their new principles and examine ?ndings. This 12 months we bought 113 submissions from 18 international locations and areas in Asia, Australasia, Europe and North the United States. The submissions went via a r- orous double-blind reviewing technique. such a lot submissions got 4 studies, a couple of submissions obtained ?ve stories, whereas basically a number of submissions obtained 3 stories. every one submission used to be dealt with by means of a space Chair who coordinated discussions between reviewers and made advice 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 technique. to make sure that vital revisions required by way of reviewers have been included into the ?nal permitted papers, and to permit submissions which might have - tential after a cautious revision, this 12 months we introduced a “revision double-check” method. briefly, 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 permitted paper was once tested via the realm Chair and application 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
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. In this article, we exploit some properties related to face recognition and propose the following alternative approach; extract features from the picture that represent items known to contribute to the recognition of faces.
The hierarchical algorithm is ﬂexible in selecting features relevant for the face recognition task at hand. 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 .
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