QQ客服 官方微博 反馈留言

浏览/检索结果: 共5条，第1-5条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 15 作者:  Fu, Kaicheng;  Du, Changde;  Wang, Shengpei;  He, Huiguang 收藏  |  浏览/下载：14/0  |  提交时间：2022/12/27 Decoding  Brain modeling  Functional magnetic resonance imaging  Predictive models  Emotion recognition  Dimensionality reduction  Pattern recognition  Fine-grained emotion decoding  multi-label learning  multi-view learning  product of experts (PoEs)  variational autoencoder   An effective approach for emotion detection in multimedia text data using sequence based convolutional neural network 期刊论文 MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 卷号: 78, 期号: 20, 页码: 29607-29639 作者:  Shrivastava, Kush;  Kumar, Shishir;  Jain, Deepak Kumar 收藏  |  浏览/下载：56/0  |  提交时间：2019/12/16 Multimedia data  Emotion detection  Deep learning  Convolutional neural network  Deep neural network   Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation 期刊论文 ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 卷号: 8, 期号: 1 作者:  Yang, Tengfei;  Xie, Jibo;  Li, Guoqing;  Mou, Naixia;  Li, Zhenyu 收藏  |  浏览/下载：8/0  |  提交时间：2019/12/11 social media  big data  fine-grained emotion classification  spatio-temporal analysis  hazard mitigation   Constructing a Chinese Conversation Corpus for Sentiment Analysis 会议论文 Dalian, China, November 8-12, 2017 作者:  Zhou, Yujun;  Li, Changliang;  Xu, Bo;  Xu, Jiaming;  Yang, Lei 收藏  |  浏览/下载：12/0  |  提交时间：2017/09/21 Chinese Conversation  Short Text  Sentiment Analysis  Machine Learning  Deep Neural Networks   Constructing a Chinese Conversation Corpus for Sentiment Analysis 会议论文 Dalian, China, November 8-12, 2017 作者:  Zhou, Yujun;  Li, Changliang;  Xu, Bo;  Xu, Jiaming;  Yang, Lei 收藏  |  浏览/下载：0/0  |  提交时间：2020/10/27 Chinese Conversation  Short Text  Sentiment Analysis  Machine Learning  Deep Neural Networks