Titlebook: Industrial Applications of Semantic Web; Proceedings of the 1 Max Bramer,Vagan Terziyan Conference proceedings 2005 IFIP International Fede

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Mast-Cell

Andrea de Polos that learning such discriminative projections locally while organizing the database hierarchically leads to a more accurate and efficient system. The proposed method is validated on the standard Labeled Faces in the Wild (LFW) benchmark dataset with millions of additional distracting face images c

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R. Rajugan,Elizabeth Chang,Tharam S. Dillon,Feng Ling,Carlo Wouterss that learning such discriminative projections locally while organizing the database hierarchically leads to a more accurate and efficient system. The proposed method is validated on the standard Labeled Faces in the Wild (LFW) benchmark dataset with millions of additional distracting face images c

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or from stereo pairs based on linear programming (.) is presented. In the presence of outliers, the new . estimator provides better results than maximum likelihood estimators such as weighted least squares, and is usually almost as good as robust estimators such as least-median-of-squares (.). In th

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e model allows for a class of transformations, such as affine and non-rigid transformations, and induces a similarity measure between shapes. The matching process is formulated in the EM algorithm. To have a fast algorithm and avoid local minima, we show how the EM algorithm can be approximated by u

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Alain Léger,Lyndon J.B. Nixon,Pavel Shvaiko,Jean Charlet a manifold in an unsupervised manner. However, the representations from unsupervised learning are not always optimal in discriminating capability. In this paper, a novel algorithm is introduced to conduct discriminant analysis in term of the embedded manifold structure. We propose a novel clusterin