Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

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书目名称Neural Information Processing影响因子(影响力)




书目名称Neural Information Processing影响因子(影响力)学科排名




书目名称Neural Information Processing网络公开度




书目名称Neural Information Processing网络公开度学科排名




书目名称Neural Information Processing被引频次




书目名称Neural Information Processing被引频次学科排名




书目名称Neural Information Processing年度引用




书目名称Neural Information Processing年度引用学科排名




书目名称Neural Information Processing读者反馈




书目名称Neural Information Processing读者反馈学科排名

小臼

GOUGE

EXALT

Neural Information Processing978-3-030-04212-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

Amplify

Multi-label Feature Selection Method Combining Unbiased Hilbert-Schmidt Independence Criterion with ely dealt with via feature selection procedure. Unbiased Hilbert-Schmidt independence criterion (HSIC) is a kernel-based dependence measure between feature and label data, which has been combined with greedy search techniques (e.g., sequential forward selection) to search for a locally optimal featu

Apoptosis

Anthropometric Features Based Gait Pattern Prediction Using Random Forest for Patient-Specific Gait and personalized gait trajectories designed for robot assisted gait training are very important for improving the therapeutic results. Meanwhile, it has been proved that human gaits are closely related to anthropometric features, which however has not been well researched. Therefore, a method based

Suppository

Robust Multi-view Features Fusion Method Based on CNMFmultiple views to obtain the new feature representation of the object using a right model. In practical applications, Collective Matrix Factorization (CMF) has good effects on the fusion of multi-view data, but for noise-containing situations, the generalization ability is poor. Based on this, the p

大暴雨

Hemoptysis

An Effective Discriminative Learning Approach for Emotion-Specific Features Using Deep Neural Networdering certain tasks from achieving better performance. Therefore, automatically learning a good representation that disentangles these components is non-trivial. In this paper, we propose a hierarchical method to extract utterance-level features from frame-level acoustic features using deep neural

挡泥板

Convolutional Neural Network with Spectrogram and Perceptual Features for Speech Emotion Recognition perceptual features such as low-level descriptors (LLDs) and their statistical values were not utilized sufficiently in CNN-based emotion recognition. To solve this problem, we propose novel features to combine spectrogram and perceptual features in different levels. Firstly, frame-level LLDs are a