支柱 发表于 2025-3-28 16:06:05
http://reply.papertrans.cn/47/4640/463949/463949_41.pngMast-Cell 发表于 2025-3-28 18:54:32
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 cKIN 发表于 2025-3-29 02:20:19
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 cRelinquish 发表于 2025-3-29 04:46:29
http://reply.papertrans.cn/47/4640/463949/463949_44.pngDeduct 发表于 2025-3-29 07:18:22
http://reply.papertrans.cn/47/4640/463949/463949_45.pngperiodontitis 发表于 2025-3-29 13:47:08
http://reply.papertrans.cn/47/4640/463949/463949_46.pngcorpus-callosum 发表于 2025-3-29 17:53:06
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 thprediabetes 发表于 2025-3-29 21:12:27
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头盔 发表于 2025-3-30 00:09:37
http://reply.papertrans.cn/47/4640/463949/463949_49.pngAmylase 发表于 2025-3-30 06:14:24
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