Titlebook: Biometric Recognition; 12th Chinese Confere Jie Zhou,Yunhong Wang,Shiqi Yu Conference proceedings 2017 Springer International Publishing AG

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书目名称Biometric Recognition影响因子(影响力)




书目名称Biometric Recognition影响因子(影响力)学科排名




书目名称Biometric Recognition网络公开度




书目名称Biometric Recognition网络公开度学科排名




书目名称Biometric Recognition被引频次




书目名称Biometric Recognition被引频次学科排名




书目名称Biometric Recognition年度引用




书目名称Biometric Recognition年度引用学科排名




书目名称Biometric Recognition读者反馈




书目名称Biometric Recognition读者反馈学科排名

Resign

Situating Language Learning in Study Abroad,sing number of depression patients, it has aroused the attention of researchers in this field. An effective and reliable machine learning based system has been expected to facilitate automated depression diagnose. This paper presents a novel deep transformation learning (DTL) method for visual-based

Melodrama

诙谐

Incommensurate

Zhu Hua,Paul Seedhouse,Vivian Cooked as a linear combination of training samples. Then the classification decision is made by evaluating which class leads to the minimum class-wise representation error. However, these two steps have different goals. The representation step prefers accuracy while the decision step requires discrimina

滔滔不绝地讲

伦理学

鞭打

Socializing Second Language Acquisitiongiven a 3D face scan, we first run the pre-processing pipeline and detect three main facial landmarks (i.e., nose tip and two inner eye corners). Then, harmonic mapping is employed to map the 3D coordinates and differential geometry quantities (e.g., normal vectors, curvatures) of each 3D face scan

完成

Reading Practices in Academic Settings,ties, has become more important due to its scientific challenges and application potentials. In this paper, we propose a novel and effective approach, which adapts the Deep Canonical Correlation Analysis (Deep CCA) network to such an issue. Two solutions are presented to speed up the training proces

eucalyptus

https://doi.org/10.1007/978-3-319-63239-1ng period of our algorithm. The first one finds the optimal parameters of supervised deep CNN by given the label distribution of the training sample as the ground truth, while the second one estimates the variances of label distribution to fit the output of the CNN. These two tasks are performed alt