標(biāo)題: Titlebook: Data Mining and Big Data; 7th International Co Ying Tan,Yuhui Shi Conference proceedings 2022 The Editor(s) (if applicable) and The Author( [打印本頁(yè)] 作者: probiotic 時(shí)間: 2025-3-21 18:30
書目名稱Data Mining and Big Data影響因子(影響力)
書目名稱Data Mining and Big Data影響因子(影響力)學(xué)科排名
書目名稱Data Mining and Big Data網(wǎng)絡(luò)公開度
書目名稱Data Mining and Big Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Mining and Big Data被引頻次
書目名稱Data Mining and Big Data被引頻次學(xué)科排名
書目名稱Data Mining and Big Data年度引用
書目名稱Data Mining and Big Data年度引用學(xué)科排名
書目名稱Data Mining and Big Data讀者反饋
書目名稱Data Mining and Big Data讀者反饋學(xué)科排名
作者: RENIN 時(shí)間: 2025-3-21 21:35 作者: sigmoid-colon 時(shí)間: 2025-3-22 00:53 作者: Urea508 時(shí)間: 2025-3-22 06:41 作者: 作繭自縛 時(shí)間: 2025-3-22 11:27 作者: 悶熱 時(shí)間: 2025-3-22 14:40 作者: 悶熱 時(shí)間: 2025-3-22 18:21 作者: 一回合 時(shí)間: 2025-3-22 23:18
Flow Prediction via?Multi-view Spatial-Temporal Graph Neural Network get the final prediction results. Comprehensive experiments on two datasets showed that the proposed framework has very high prediction performance, and outperforms the baseline model by more than 6%.作者: 厭惡 時(shí)間: 2025-3-23 05:09 作者: Communal 時(shí)間: 2025-3-23 07:50
Research and Analysis of Video-Based Human Pose Estimationally constructed features of pose angles, relative displacements, moving pattern sequences and etc. are calculated. These informative features enlighten people of latent motion rules. Finally, we report how our framework is applied to realistic classification datasets. Through our work, an overall s作者: 不感興趣 時(shí)間: 2025-3-23 13:05
Data Mining and Big Data978-981-19-9297-1Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: tattle 時(shí)間: 2025-3-23 15:25
Communications in Computer and Information Sciencehttp://image.papertrans.cn/d/image/262916.jpg作者: MULTI 時(shí)間: 2025-3-23 21:12 作者: 偽證 時(shí)間: 2025-3-23 22:25
A Note on Quantification and Modalities, and defender) in a 3D space. The targeter tries to fly as quickly as possible from a starting point to the terminal, while the defender seeks to protect it from the attacker. The problem is difficult to solve under traditional game theory methods, while deep reinforcement learning (DRL) has shown 作者: manifestation 時(shí)間: 2025-3-24 02:43
L’?uvre algébrique de Charles Fran?ois Sturmnt learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of “exploration” and “exploitation”, and the effect of作者: dry-eye 時(shí)間: 2025-3-24 07:25
MM. Vecten,MM. Querret,MM. Vernier,Ch. Sturmmissions. However, while existing methods include some information from the physical and social domains, they do not provide a comprehensive representation of cyberspace. Existing reasoning methods are also based on expert given rules, resulting in inefficiency and a low degree of intelligence. To a作者: Strength 時(shí)間: 2025-3-24 13:01 作者: 恩惠 時(shí)間: 2025-3-24 17:02 作者: Counteract 時(shí)間: 2025-3-24 20:16 作者: 容易做 時(shí)間: 2025-3-24 23:44 作者: Tincture 時(shí)間: 2025-3-25 05:56
Collective Action for Social Changemness of following friends form the diverse local structures of the same person, leading to a high degree of non-isomorphism across networks. The edges resulting in non-isomorphism are harmful to learn consistent representations of one natural person across networks, i.e., the structural “noisy data作者: Relinquish 時(shí)間: 2025-3-25 09:36
Collective Action for Social Change widely used to process large graphs. Graph partitioning is critical in parallel and distributed graph processing systems because it can balance the computational load and reduce communication load. An efficient graph partitioning algorithm can significantly improve the performance of large-scale gr作者: 火花 時(shí)間: 2025-3-25 14:42
https://doi.org/10.1007/978-3-319-15515-9obile devices with efficient inference a major challenge for industry today. Low-precision quantization is one of the key methods to achieve efficient inference on complex networks, but previous works often quantize partial layers because severe accuracy degradation occurs when quantizing is applied作者: KEGEL 時(shí)間: 2025-3-25 16:49
Anika Fiebich,Nhung Nguyen,Sarah Schwarzkopf cross-domain research. It has become a hot research topic to apply Convolutional neural network (CNN) to emotion recognition based on EEG signals. Although many researchers have used experiments showing that CNNs have good results for emotion recognition, they ignore the individual differences of s作者: Iatrogenic 時(shí)間: 2025-3-25 22:52 作者: 壯觀的游行 時(shí)間: 2025-3-26 02:45
Collective Bargaining in Labour Law Regimesn addition, ubiquitous video software and monitoring machines provide sufficient video data, and all kinds of key elements can be found in the visual information. However, due to different task subdivision scenarios as well as the confusing nature of the human actions, the scenario-oriented video de作者: Itinerant 時(shí)間: 2025-3-26 07:29
Collective Bargaining in Labour Law Regimesbased on ResNet and rule matching is proposed in this paper. The militant‘s action classification is done by 2 levels of classification. Firstly, the skeleton key points are extracted from the militant‘s combat video frames by OpenPose. Then, the first level classification of militant‘s action is pe作者: Bouquet 時(shí)間: 2025-3-26 08:35 作者: Curmudgeon 時(shí)間: 2025-3-26 16:07 作者: 情節(jié)劇 時(shí)間: 2025-3-26 18:40 作者: 地名詞典 時(shí)間: 2025-3-26 22:42 作者: aesthetician 時(shí)間: 2025-3-27 01:08
Conference proceedings 2022lume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc..作者: 性上癮 時(shí)間: 2025-3-27 07:56 作者: 我不重要 時(shí)間: 2025-3-27 12:06
Conference proceedings 2022g, China,?in November 21–24, 2022..The 62 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 135 submissions. The papers present the latest research on?advantages in theories, technologies, and applications in data mining and big data. The vo作者: Nefarious 時(shí)間: 2025-3-27 14:09 作者: gerontocracy 時(shí)間: 2025-3-27 19:40
https://doi.org/10.1007/978-3-031-12334-4ion method based on attentive relational state representation establishes an insensitive state representation to permutation and problem scale. In our experiments on Intelligent Joint Operation Simulation, experimental results show that attentive relational state representation improves the baseline performance.作者: PHAG 時(shí)間: 2025-3-27 22:51 作者: 節(jié)省 時(shí)間: 2025-3-28 02:51 作者: 材料等 時(shí)間: 2025-3-28 08:34
Collective Action for Social Change sharing encoder and graph neural network for structure denoising are learned using an iterative learning schema. Experiments on real-world datasets demonstrate the outperformance of the proposed framework in terms of efficiency and transferability.作者: beta-carotene 時(shí)間: 2025-3-28 11:35 作者: Commentary 時(shí)間: 2025-3-28 16:09
Roland Maximilian Happach,Meike Tilebeinull use of convolution to extract spatial features and use LSTM to obtain temporal features. With this model, we can predict 3D human posture through 2D sequences. Compared with the previous work on classical data sets, our method has good detection results.作者: AMPLE 時(shí)間: 2025-3-28 19:59 作者: occurrence 時(shí)間: 2025-3-29 01:08
A Deep Reinforcement Learning Approach for?Cooperative Target Defense state space, action space, and rewards of the agents. Three kinds of reward functions are proposed for the attacker and compared by experimental results. Our study provides a good foundation for the cooperative target defense problem.作者: 過(guò)份好問(wèn) 時(shí)間: 2025-3-29 06:51
RotatSAGE: A Scalable Knowledge Graph Embedding Model Based on Translation Assumptions and Graph Neu eliminate redundant entity information and simplify the proposed model. In the experiments, the link prediction task is used to evaluate the performance of embedding models. The experiments on four benchmark datasets show the overall performance of RotatSAGE is higher than baseline models.作者: plasma-cells 時(shí)間: 2025-3-29 09:26
Denoise Network Structure for?User Alignment Across Networks via?Graph Structure Learning sharing encoder and graph neural network for structure denoising are learned using an iterative learning schema. Experiments on real-world datasets demonstrate the outperformance of the proposed framework in terms of efficiency and transferability.作者: 柔聲地說(shuō) 時(shí)間: 2025-3-29 12:20 作者: 狂怒 時(shí)間: 2025-3-29 16:04
Pose Sequence Model Using the?Encoder-Decoder Structure for?3D Pose Estimationull use of convolution to extract spatial features and use LSTM to obtain temporal features. With this model, we can predict 3D human posture through 2D sequences. Compared with the previous work on classical data sets, our method has good detection results.作者: Constituent 時(shí)間: 2025-3-29 20:36
Action Recognition for Solo-Militant Based on ResNet and Rule Matchingtion is output according to the 2 levels of classification. The experimental results show that the proposed method in this paper can achieve more effective recognition rate of solo-militant action under small sample data set.作者: monochromatic 時(shí)間: 2025-3-30 00:21 作者: Licentious 時(shí)間: 2025-3-30 05:28 作者: crucial 時(shí)間: 2025-3-30 08:33
Anika Fiebich,Nhung Nguyen,Sarah Schwarzkopfrences and temporal differences with optimal scale convolution, which solves restrictions of the results when classifying. The experiments on public DEAP dataset show that the 1D multi-scale CNN proposed outperforms other existing models.作者: FAST 時(shí)間: 2025-3-30 13:16 作者: euphoria 時(shí)間: 2025-3-30 17:51 作者: gorgeous 時(shí)間: 2025-3-30 21:38
Particle Swarm Based Reinforcement Learning particle swarm based reinforcement learning framework (PRL). Compared with the standard reinforcement learning algorithms, this framework greatly improves the exploration ability and obtains better scores in a series of gym experimental tests.作者: 紡織品 時(shí)間: 2025-3-31 02:32
Emotion Recognition Based on?Multi-scale Convolutional Neural Networkrences and temporal differences with optimal scale convolution, which solves restrictions of the results when classifying. The experiments on public DEAP dataset show that the 1D multi-scale CNN proposed outperforms other existing models.作者: exigent 時(shí)間: 2025-3-31 06:33
Multiple Residual Quantization of?Pruninghts by combining the low-bit weights stem and residual parts many times, to minimize the error between the quantized weights and the full-precision weights, and to ensure higher precision quantization. At the same time, MRQP prunes some weights that have less impact on loss function to further reduce model size.作者: 演講 時(shí)間: 2025-3-31 11:11
Heterogeneous Multi-unit Control with?Curriculum Learning for?Multi-agent Reinforcement Learningnctions. Methods that utilize parameter or replay-buffer sharing are able to address the problem of combinatorial explosion under isomorphism assumption, but may lead to divergence under heterogeneous setting. This work use curriculum learning to bypass the barrier of a needle in a haystack that is 作者: 角斗士 時(shí)間: 2025-3-31 15:17 作者: addition 時(shí)間: 2025-3-31 18:23
Particle Swarm Based Reinforcement Learningnt learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of “exploration” and “exploitation”, and the effect of
