Proceedings of the 2019 4th International Conference on Machine Learning Technologies最新文献_第2页

Attention-Based Bidirectional Long Short-Term Memory Networks for Chinese Named Entity Recognition 基于注意的中文命名实体识别双向长短期记忆网络

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341002

Chaoyi Huang, Youguang Chen, Qi Liang

{"title":"Attention-Based Bidirectional Long Short-Term Memory Networks for Chinese Named Entity Recognition","authors":"Chaoyi Huang, Youguang Chen, Qi Liang","doi":"10.1145/3340997.3341002","DOIUrl":"https://doi.org/10.1145/3340997.3341002","url":null,"abstract":"Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades. In this paper, we investigate the problem of Chinese named entity recognition. Using attention mechanisms based on BiLSTM-CRF model, a model is proposed in this paper, which makes better use of word-based and character-based information. All the potential words that match the input characters and sentences with the dictionary are encoded, and one attention layer to control the dynamic acquisition of multiple potential characters in different paths from sequence information. A series of input characters and all potential words matched with dictionaries in sentences are encoded to measure the correlation scores between candidate characters and potential words. Another attention layer is to produce a weight vector and merge word-level features from each time step into a sentence-level feature vector by multiplying the weight vector. Then, CRF model is introduced to get the final tagging to obtain the desired result. The experimental data shows that the F1-score of our model has increased from 73.88% to 75.10% on the OntoNote 4 dataset, and from 93.18% to 94.17% on the MSRA dataset. The results show that our method has a better performance than the previous model.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115747331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Vision-Based Mobile People Counting System 基于视觉的移动人口计数系统

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3340999

Xu Yang, J. Gaspar, W. Lou, W. Ke, C. Lam, Yapeng Wang

{"title":"Vision-Based Mobile People Counting System","authors":"Xu Yang, J. Gaspar, W. Lou, W. Ke, C. Lam, Yapeng Wang","doi":"10.1145/3340997.3340999","DOIUrl":"https://doi.org/10.1145/3340997.3340999","url":null,"abstract":"People detection and counting systems are highly valuable in multiple situations including managing emergency situations and efficiently allocating resources. However, most people counting systems are based on fixed sensors or fixed cameras, which lack flexibility and convenience. In this paper, we have developed a vision-based mobile people counting system which uses Android smartphones to capture images, and state-of-the-art person detectors, based on artificial intelligence, to count the number of people in a designated area. The embedded devices in smartphones such as camera, clock, GPS, are utilized to provide additional information for data collection. Several person detection frameworks such as You Only Look Once v2 (YOLO2), Aggregate Channel Features (ACF) and Multi-Task cascade Convolutional Neural Network (MTCNN) were evaluated to determine the best performing algorithm capable of offering accurate counting results across different scenarios. The experiments results show that YOLO2 outperforms ACF and MTCNN detection algorithms in different scenarios. However, YOLO2 has its own limitations as it often outputs redundant detections, requiring an additional Non-Maxima Suppression (NMS) algorithm to output a single bounding box per detection. The NMS threshold has to be carefully pre-fixed to provide top detection and counting performance across different scenarios.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123819218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Distribution Based Weighted Extreme Learning Machine 基于数据分布的加权极限学习机

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3340998

Meiyi Li, Qingshuai Sun, Xingwang Liu

{"title":"Data Distribution Based Weighted Extreme Learning Machine","authors":"Meiyi Li, Qingshuai Sun, Xingwang Liu","doi":"10.1145/3340997.3340998","DOIUrl":"https://doi.org/10.1145/3340997.3340998","url":null,"abstract":"As an effective learning approach, extreme learning machine (ELM) has been applied to multiple fields with its faster learning speed and better generalization performance. To solve the classification problem especially the data with imbalanced class distribution, some solutions are proposed based on cost sensitive ELM. However, the existing methods only consider the effect of the misclassified sample on the class to which it belongs but ignoring the overall loss. In this paper, we propose a new weighting scheme used in ELM, data distribution based weighted extreme learning machine (D-WELM) for binary and multiclass classification problems with imbalanced data distributions. It is noteworthy that the proposed method maintains the advantages from original ELM. D-WELM considers not only the effect of sample sizes in each class, but also class distribution. Meanwhile, this work takes overall loss into account. Experimental results show that D-WELM can achieve better performance for classification problems with imbalanced data distributions than original ELM, weighted ELM(WELM) and class-specific cost regulation ELM (CCR-ELM). In addition, D-WELM with kernel can also get good performance.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123819554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structured Processing Method of Medical Examination Text Reports Based on Tree Model 基于树模型的医学检查文本报告结构化处理方法

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341001

Duan Yifan, Pan Qiao, Golddy Indra Kumara, Dehua Chen

引用次数: 2

Cascade Evolving Network for Vehicle Detection of Highway 高速公路车辆检测的级联进化网络

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341011

Xuan Cai, Huayu Li, Li Wang

{"title":"Cascade Evolving Network for Vehicle Detection of Highway","authors":"Xuan Cai, Huayu Li, Li Wang","doi":"10.1145/3340997.3341011","DOIUrl":"https://doi.org/10.1145/3340997.3341011","url":null,"abstract":"In this paper, a novel vehicle detection scheme via Cascade Evolving Network (CEN) is presented, which is designed for our highway vehicle detection dataset captured from super wide-angle lens. The highway images are in multi-scale, and almost all cars are dense and seriously obscured. To handle such obstacles, CEN makes better use of contextual information by proposing and refining the object boxes under different feature representations. Specifically, our framework is embedded as a light-weight cascade network. First a Light-weight Parallel Network (LPN) with a small Intersection Over Union (IOU) is applied for extracting multi-scale feature map. The parallel two networks, Coarse-grained Network (CgN) with a smallest IOU and Fine-grained Network (FgN) with a bit larger IOU produce multi-scale candidate boxes with various settings of prior anchors. The smallest IOU is designed for small objects whose IOU is smaller than large ones. Another two subnetworks refine the vague edges of proposals afterwards with gradual increasing IOU. For maximizing contextual information, three subnetworks connect together. Meanwhile, a new novel feature fusion method, named Grouped Region Proposal Network (GRPN) is adopted. CEN achieves the promising results on our highway vehicle detection dataset. To verify the robustness of the network, an evaluation on the DETRAC benchmark dataset is implemented, and obtain a significant improvement over the baseline model of Faster RCNN by 13.11% for mAP. This shows that the initial boxes can be better refined for both localization and recognition in CEN. Furthermore, Our network achieves 7-11 FPS detection speed on a moderate commercial GPU, which is much more effective than the baseline model.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131656785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Support Vector Data Description with Fractional Order Kernel 分数阶核支持向量数据描述

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341003

Changming Zhu, Zhe Wang, Minguang Wang, Wenbo Jie, Daqi Gao

引用次数: 0