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

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書目名稱Biometric Recognition影響因子(影響力)




書目名稱Biometric Recognition影響因子(影響力)學(xué)科排名




書目名稱Biometric Recognition網(wǎng)絡(luò)公開度




書目名稱Biometric Recognition網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Biometric Recognition被引頻次




書目名稱Biometric Recognition被引頻次學(xué)科排名




書目名稱Biometric Recognition年度引用




書目名稱Biometric Recognition年度引用學(xué)科排名




書目名稱Biometric Recognition讀者反饋




書目名稱Biometric Recognition讀者反饋學(xué)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

詼諧

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

滔滔不絕地講

倫理學(xué)

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