Titlebook: Schaltalgebra; für Fachschulen Tech Hermann Gschwendtner Book 1977 Springer Fachmedien Wiesbaden 1977 Algebra.Arbeit.Datenverarbeitung.Elek

hector

書目名稱Schaltalgebra影響因子(影響力)




書目名稱Schaltalgebra影響因子(影響力)學(xué)科排名




書目名稱Schaltalgebra網(wǎng)絡(luò)公開度




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




書目名稱Schaltalgebra被引頻次




書目名稱Schaltalgebra被引頻次學(xué)科排名




書目名稱Schaltalgebra年度引用




書目名稱Schaltalgebra年度引用學(xué)科排名




書目名稱Schaltalgebra讀者反饋




書目名稱Schaltalgebra讀者反饋學(xué)科排名

官僚統(tǒng)治

Stagger

Hermann Gschwendtnerher bit rate, and will reduce the efficiency of CABAC encoding. In order to decrease the bit rate without affecting the quality, we also making some modifications for the CABAC encoding. The proposed method is implemented on the Tilera-GX36 multicore platform. Experiment results show that our algori

背書

Hermann Gschwendtnerg multiple local pooling layers, we can learn the hierarchical graph structure and extract multi-level semantics. In addition, we design a multi-head attention-based semantic interaction layer to capture the interaction between selected nodes and the nodes before the local pooling layer, which addre

alabaster

Hermann Gschwendtneren datasets, where the datasets SK-LARGE, SK-SMALL (SK506), and WH-SYMMAX are commonly used for the skeleton detection task. The F-measure score obtained for these three datasets are 0.789, 0.751, and 0.865, respectively. The effectiveness of the method in this paper can be verified by ablation stud

能得到

Hermann Gschwendtnerand solve it mathematically to derive the analytical solution. Several datasets have been used to compare the performance of KPCA and our novel LPKPCA including ORL face dataset, Yale Face Dataset B and Scene 15 Dataset. All the experimental results show that our method can achieve better performanc

Cloudburst

Hermann Gschwendtnere the original 3-channel images and their corresponding GAN-generated fake images to form 6-channel representations of the dataset, hoping to address the domain shift problem while exploiting the success of available detection models. The idea of augmented data representation may inspire further stu

ironic

Hermann Gschwendtneric combination adds more potential information to each class. We name the proposed approach as L2-CVAEGAN, and conduct extensive experiments on several benchmark datasets. Compared with existing methods, these simple strategies lead to significant promotions. The comprehensive experiments of ZSL and

漂泊

us construct the unsupervised learning method through a layerwise mean. Then we further perform the method on the deep model with multiple CNN layers. Finally, the method is used for the remote sensing image representation and scenes classification. The experimental results over the public UC-Merced

硬化

978-3-528-04037-6Springer Fachmedien Wiesbaden 1977