標(biāo)題: Titlebook: Advances in Knowledge Discovery and Data Mining; 23rd Pacific-Asia Co Qiang Yang,Zhi-Hua Zhou,Sheng-Jun Huang Conference proceedings 2019 S [打印本頁] 作者: Spouse 時間: 2025-3-21 17:11
書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)
書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)學(xué)科排名
書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度
書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Knowledge Discovery and Data Mining被引頻次
書目名稱Advances in Knowledge Discovery and Data Mining被引頻次學(xué)科排名
書目名稱Advances in Knowledge Discovery and Data Mining年度引用
書目名稱Advances in Knowledge Discovery and Data Mining年度引用學(xué)科排名
書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋
書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋學(xué)科排名
作者: Meager 時間: 2025-3-21 20:54 作者: heirloom 時間: 2025-3-22 01:02 作者: 遺棄 時間: 2025-3-22 07:37
https://doi.org/10.1007/978-3-030-04972-0nities. Existing QA studies assume that questions are raised by humans and answers are generated by machines. Nevertheless, in many real applications, machines are also required to determine human needs or perceive human states. In such scenarios, machines may proactively raise questions and humans 作者: 果仁 時間: 2025-3-22 08:53
Coupling in UAV Cooperative Control, of the mainstream approaches to tackle this task. However, most of the existing studies focus on some specific kind of auxiliary data, which is usually platform- or domain- dependent. In existing works, the incorporation of auxiliary data has put limits on the applicability of the prediction model 作者: 排他 時間: 2025-3-22 14:53
https://doi.org/10.1007/978-3-319-74265-6 smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence 作者: 集合 時間: 2025-3-22 18:55
https://doi.org/10.1007/978-3-319-74265-6with the equipment’s types. Proceeding from the fundamental features of load time series, we propose a method to identify electrical equipment from power load profiles accurately. Aiming to improve the classification accuracy and generalization performance of convolutional neural network (CNN), we c作者: 階層 時間: 2025-3-22 23:42 作者: 枯萎將要 時間: 2025-3-23 03:33
https://doi.org/10.1007/978-3-319-74265-6er architecture, have achieved impressive progress in abstractive document summarization. However, the saliency of summary, which is one of the key factors for document summarization, still needs improvement. In this paper, we propose Topic Attentional Neural Network (TANN) which incorporates topic 作者: surmount 時間: 2025-3-23 05:50 作者: overweight 時間: 2025-3-23 10:53 作者: 姑姑在炫耀 時間: 2025-3-23 14:49 作者: chiropractor 時間: 2025-3-23 18:10 作者: 顧客 時間: 2025-3-24 01:16
A Dynamic Resource Management Problem,arieties of features from ECG and using them to build a classifier. However, ECG usually presents high inter- and intra-subjects variability both in morphology and timing, hence, it’s difficult for predesigned features to accurately depict the fluctuation patterns of each heartbeat. To this end, we 作者: Keratin 時間: 2025-3-24 02:47 作者: FUSE 時間: 2025-3-24 09:42
The Core, Superadditivity, and Convexity,ctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classific作者: 吞噬 時間: 2025-3-24 12:31
https://doi.org/10.1007/978-3-662-60715-2ic signal control results in people’s waste of time in commuting, road rage in the traffic jam, and high cost for city operation. Recently, deep reinforcement learning (DRL) approaches shed lights to better optimize traffic signal plans according to the feedback received from the environment. Most o作者: 恃強凌弱的人 時間: 2025-3-24 17:32 作者: 健壯 時間: 2025-3-24 22:49
A Cloud Architecture for Service Robots,ater-supply, and sales predictions. In this paper, we study the case of retailers’ sales forecasting on Tmall—the world’s leading online B2C platform. By analyzing the data, we have two main observations, i.e., . after we group different groups of retails and a . after we transform the sales (target作者: enhance 時間: 2025-3-24 23:35 作者: Judicious 時間: 2025-3-25 03:45
Advances in Knowledge Discovery and Data Mining978-3-030-16145-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 桶去微染 時間: 2025-3-25 09:07 作者: cataract 時間: 2025-3-25 11:49
Studies in Systems, Decision and Controlurally incorporated into the similarity matrix for word specificity and a restricted convolution is proposed to ease the sparsity. We compare EFCNN with a number of baseline models for expert finding including the traditional model and the neural model. Our EFCNN clearly achieves better performance than the comparison methods on three datasets.作者: glamor 時間: 2025-3-25 18:44
Accurate Identification of Electrical Equipment from Power Load Profilesets comparing with 12 existing methods. Furthermore, we compare our model with LSTM, GRU and CNN on the electrical equipment load data, which is from industries in certain area. The final results show that our model has a higher equipment identification accuracy than other deep learning models.作者: 泥沼 時間: 2025-3-25 23:27 作者: aerial 時間: 2025-3-26 04:11
The Core, Superadditivity, and Convexity,al in student’s long-term learning process. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art KT modelling techniques.作者: 惹人反感 時間: 2025-3-26 05:30 作者: Stress-Fracture 時間: 2025-3-26 10:11
0302-9743 owledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019..The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present?new ideas, original research results, and practical development experiences from all KDD related areas, incl作者: promote 時間: 2025-3-26 16:29 作者: Credence 時間: 2025-3-26 18:02 作者: 勉強 時間: 2025-3-26 21:19 作者: Incisor 時間: 2025-3-27 02:35 作者: 青少年 時間: 2025-3-27 08:41 作者: acolyte 時間: 2025-3-27 12:51
Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence 作者: 紅腫 時間: 2025-3-27 16:03 作者: Credence 時間: 2025-3-27 17:52 作者: esoteric 時間: 2025-3-27 23:44
Topic Attentional Neural Network for Abstractive Document Summarizationer architecture, have achieved impressive progress in abstractive document summarization. However, the saliency of summary, which is one of the key factors for document summarization, still needs improvement. In this paper, we propose Topic Attentional Neural Network (TANN) which incorporates topic 作者: 冰河期 時間: 2025-3-28 03:58 作者: 可能性 時間: 2025-3-28 09:49
EFCNN: A Restricted Convolutional Neural Network for Expert Findingut still heavily suffers from low matching quality due to inefficient representations for experts and topics (queries). In this paper, we present an interesting model, referred to as EFCNN, based on restricted convolution to address the problem. Different from traditional models for expert finding, 作者: GLIB 時間: 2025-3-28 13:56
CRESA: A Deep Learning Approach to Competing Risks, Recurrent Event Survival Analysisevent survival analysis in the presence of one or more . in each recurrent time-step, in order to obtain the probabilistic relationship between the input covariates and the distribution of event times. Since traditional survival analysis techniques suffer from drawbacks due to strong parametric mode作者: Stress 時間: 2025-3-28 18:36 作者: HUSH 時間: 2025-3-28 21:54
Arrhythmias Classification by Integrating Stacked Bidirectional LSTM and Two-Dimensional CNNarieties of features from ECG and using them to build a classifier. However, ECG usually presents high inter- and intra-subjects variability both in morphology and timing, hence, it’s difficult for predesigned features to accurately depict the fluctuation patterns of each heartbeat. To this end, we 作者: myocardium 時間: 2025-3-29 00:18
An Efficient and Resource-Aware Hashtag Recommendation Using Deep Neural Networkscommendation system is called HAZEL (HAshtag ZEro-shot Learning). Selecting right hashtags can increase exposure and attract more fans on a social media platform. With the help of the state-of-the-art deep learning technologies such as Convolutional Neural Network (CNN), the recognition accuracy has作者: 形上升才刺激 時間: 2025-3-29 03:40
Dynamic Student Classiffication on Memory Networks for Knowledge Tracingctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classific作者: Comedienne 時間: 2025-3-29 10:30
Targeted Knowledge Transfer for Learning Traffic Signal Plansic signal control results in people’s waste of time in commuting, road rage in the traffic jam, and high cost for city operation. Recently, deep reinforcement learning (DRL) approaches shed lights to better optimize traffic signal plans according to the feedback received from the environment. Most o作者: critic 時間: 2025-3-29 13:08
Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility this model has been hindered by the following two limitations: (.) computational expensiveness of the model and (.) infrequent itemsets may be output as high utility itemsets. This paper makes an effort to address these two limitations. A generic high utility-frequent itemset model is introduced to作者: 愚笨 時間: 2025-3-29 15:55 作者: 共同生活 時間: 2025-3-29 23:28 作者: Abnormal 時間: 2025-3-30 01:58
Semi-interactive Attention Network for Answer Understanding in Reverse-QA and propose an improved structure called semi-interactive attention network (Semi-IAN). Two Chinese data sets for rQA are compiled. We evaluate several conventional text classification models for comparison, and experimental results indicate the promising performance of our proposed models.作者: foliage 時間: 2025-3-30 04:55
Neural Network Based Popularity Prediction by Linking Online Content with Knowledge Basesng KB embedding of the target entity and popularity dynamics from items with similar entity information. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.作者: ungainly 時間: 2025-3-30 09:29
Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networksformation or historical demand and utilize Convolutional Neural Networks (CNN) to extract their spatial correlations. Second, we map external factors to future demand levels as part of the multi-task learning framework to further boost prediction accuracy. We conduct experiments on a large-scale rea作者: effrontery 時間: 2025-3-30 15:04
Similarity-Aware Deep Attentive Model for Clickbait Detectionuality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods.作者: inhibit 時間: 2025-3-30 18:50
Topic Attentional Neural Network for Abstractive Document Summarizationct experiments on the CNN/Daily Mail dataset. The results show our model obtains higher ROUGE scores and achieves a competitive performance compared with the state-of-the-art abstractive and extractive models. Human evaluation also demonstrates our model is capable of generating summaries with more 作者: amorphous 時間: 2025-3-31 00:32 作者: amputation 時間: 2025-3-31 02:57 作者: 高貴領(lǐng)導(dǎo) 時間: 2025-3-31 07:37 作者: 光亮 時間: 2025-3-31 12:14
Arrhythmias Classification by Integrating Stacked Bidirectional LSTM and Two-Dimensional CNNesign a discrete wavelet transform (DWT) based ECG decomposition layer and a Sum Rule based intermediate classification result fusion layer, by which ECG can be analyzed from multiple time-frequency resolutions, and the classification results of our model can be more accurate. Experimental results b
