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

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書(shū)目名稱Neural Information Processing影響因子(影響力)




書(shū)目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書(shū)目名稱Neural Information Processing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Neural Information Processing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Neural Information Processing被引頻次




書(shū)目名稱Neural Information Processing被引頻次學(xué)科排名




書(shū)目名稱Neural Information Processing年度引用




書(shū)目名稱Neural Information Processing年度引用學(xué)科排名




書(shū)目名稱Neural Information Processing讀者反饋




書(shū)目名稱Neural Information Processing讀者反饋學(xué)科排名

小臼

GOUGE

EXALT

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

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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

擋泥板

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