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检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是21-30 订阅

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LDRC: Long-tail Distantly Supervised Relation Extraction via Contrastive learning 23

LDRC: Long-tail Distantly Supervised Relation Extraction via...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Li, Tingwei Wang, Zhi Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450398633

Long-tail problem is one of the major challenges in distantly supervised relation extraction. Some recent works on the long-tail problem attempt to transfer knowledge from data-rich and semantically similar head classes to data-poor tail classes using a relation hierarchical tree. These methods, however, are based on the assumption that long-tail and head relations have a strong correlation, which does not always hold true, and the model's ability to learn long-tail relations is essentially not improved. In this paper, a novel joint learning framework that combines relation extraction and contrastive learning is proposed, allowing the model to directly learn the subtle differences between different categories to improve long-tail relation extraction. Experimental results show that our proposed model outperforms the current state-of-the-art (SOTA) model on various mainstream datasets. © 2023 ACM.

关键词： Extraction

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Insight Evaluation on Traditional and CNN Features 2022

Insight Evaluation on Traditional and CNN Features

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Truong-Minh Le Nguyen-Phan-Long Le Nhat-Gia-Nghi Hua Nhien-Loc Bui Vinh Quang Dinh Univ Informat Technol VNU HCM Ho Chi Minh City Vietnam Vietnamese Germany Univ Binh Duong Vietnam

ISBN: (纸本)9781450387477

Feature extraction serves as the prerequisite for any intelligent-based applications. There are various methods to extract features from the initial data sets, namely traditional filters and deep learning components. However, current traditional feature extraction methods still have troubles in dealing with automatically updating the weights. This paper will propose a new feature representation method that shows more promising results than any previous methods. This proposed method investigates the benefits of features based on Convolutional Neural Network (CNN) to solve the above problem. CNN's purpose is to process multi-dimensional data, such as image and time-series data and crucial features are noticed and picked automatically without the need of being supervised by human-being. The proposed method has demonstrated significant improvement in terms of accuracy and stability compared to traditional methods of feature extraction. From that, we can easily understand why CNN-based algorithms have become a part of state-of-the-art algorithms these days.

关键词： Convolutional Neural Network Deep learning Feature Extraction Local Patterns Traditional Filters

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Deep Reinforcement learning with Noisy Exploration for Autonomous Driving 2022

Deep Reinforcement Learning with Noisy Exploration for Auton...

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Li, Ruyang Zhang, Yaqiang Zhao, Yaqian Wei, Hui Xu, Zhe Zhao, Kun Inspur Beijing Elect Informat Ind Co Ltd Beijing Peoples R China Shandong Mass Informat Technol Res Inst State Key Lab High End Server & Storage Technol Jinan Peoples R China Inspur Elect Informat Ind Co Ltd State Key Lab High End Server & Storage Technol Jinan Peoples R China Inspur Elect Informat Ind Co Ltd Jinan Peoples R China

ISBN: (纸本)9781450387477

Autonomous driving decision-making is a great challenge in complex traffic environment, and the deep reinforcement learning (DRL) can contribute to the more intelligent strategy. In the autonomous driving scenarios with DRL algorithms, sufficient exploration to the traffic environment is vital for constructing the state spaces, training the driving decision model and transferring to a new environment. In this paper, three different noise modes are presented to investigate the performance of noisy exploration and generalization in self-driving tasks. Extensive experiments indicate that the noisy exploration is not necessary for the easy traffic environments, and the correlated noisy exploration is an effective technique in generalizing to complex traffic environments, while the uncorrelated noisy exploration may result in a counter-productive effect in inertial autonomous driving system.

关键词： Autonomous Driving Deep Reinforcement learning Noisy Exploration Generalization

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Personnel status detection model suitable for vertical federated learning structure 2022

Personnel status detection model suitable for vertical feder...

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Ji, Jie Yan, Danfeng Mu, Zhengyang Beijing Univ Posts & Telecommun 10 Xitucheng Rd Beijing 100876 Peoples R China

ISBN: (纸本)9781450387477

With the improvement of the medical system, the universal access of wearable devices and people's greater concern about personal health, personnel health detection has received greater attention. However, existing personnel status detection faces the problem that existing models cannot get more accurate results by multiple types of data because the data is difficult to obtain across domains due to privacy issue. In this paper, we propose a vertical federated learning model for person status detection using multimodal data. Considering the varying compute capability of training devices and the different inference speed caused by modality complexity, a lightweight feature extraction sub-model is used, while ensuring data privacy and tolerating accuracy degradation. Also, we design a Fast and Secure (FS) module that effectively reduces the amount of data onto the network transmission process, the model's dependence on the network and the risk of data being intercepted by the third party. Experiment results demonstrate the feasibility of multimodal vertical federated learning and the accuracy is increased by 5.6 percentage points compared with unimodal. The FS module reduces the amount of network transmission information by 50% and 48.1% during model use and model training, respectively

关键词： Multi-modal Vertical federated learning Personnel status detection

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Retailers' Order Decision with Setup Cost using machine learning 24

Retailers' Order Decision with Setup Cost using Machine Lear...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Jintanasonti, Pissacha Dumrongsiri, Aussadavut Tantiwattanakul, Phattarasaya Sirindhorn International Institute of Technology Thammasat University Thailand King Mongkut's University of Technology North Bangkok Thailand

ISBN: (纸本)9798400716546

The objective of this study was to gain valuable insights into retailer behavior and develop a predictive model to inform their purchasing decisions. This process involved a comprehensive analysis of the various factors that influence retailers when they make choices regarding product or service purchases. To gather the data for the study, a simple random sampling method was employed to extract retailer purchase data from a mathematical model using Excel Solver. While Excel Solver can determine optimal solutions quickly but, with hundreds of retailers, reoptimizing the model every time a price is changed it is not practical and the manufacturer must contact trial and error many times to optimize has price. To address this challenge, Artificial Neural Network techniques were utilized to analyze the sample data. The resulting equations were subsequently integrated into manufacturer model to assist manufacturers in forecasting retailer decisions. With an understanding of the expected patterns of retailer behavior, manufacturers can strategically plan their purchase orders to align with different promotion and marketing strategies. The study demonstrated that the model achieved an average minimum cost increase of 5.02% when tested with a new dataset consisting of 5,000 retailers. Based on these findings, it is recommended that manufacturers adjust their order policies by placing orders at the beginning of period 2 and making the most of the earliest discount period. Furthermore, manufacturers should consider a range of factors, including total holding cost, reorder cost, and expected demand, when formulating their order policies. This comprehensive approach will help manufacturers optimize their purchasing decisions and enhance their overall operational efficiency. © 2024 Owner/Author.

关键词： machine learning

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A Review of Unsupervised Anomaly Detection Techniques for Health Insurance Fraud 10

A Review of Unsupervised Anomaly Detection Techniques for He...

引用

IEEE 10th international conference on Big Data computing Service and machine learning Applications (IEEE BigDataService)

作者： Leevy, Joffrey L. Salekshahrezaee, Zahra Khoshgoftaar, Taghi M. Florida Atlantic Univ Boca Raton FL 33431 USA

ISBN: (纸本)9798350366396;9798350366389

This paper surveys the latest unsupervised anomaly detection methodologies applied to health insurance fraud, covering studies from 2017 to 2024. Our review includes a variety of machine-learning approaches, evaluating their effectiveness in handling complex, high-dimensional, and imbalanced healthcare datasets. Techniques such as Isolation Forest, Bayesian hierarchical models, and deep autoencoders demonstrate superior performance compared to traditional methods. Despite significant advancements, gaps remain with regard to transfer learning, interpretability and explainability of models, and the development of real-time, incremental learning algorithms. Future research should focus on these areas to enhance fraud detection accuracy and trust. Our work aims to provide a valuable resource for researchers and practitioners, supporting the development of more robust and adaptive fraud detection systems to protect healthcare integrity and reduce financial losses.

关键词： anomaly detection health insurance fraud unsupervised learning machine learning big data analytics

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Contrastive learning for Event Extraction 2022

Contrastive Learning for Event Extraction

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6th international conference on machine learning and soft computing (icmlsc)

作者： Yao, Shunyu Yang, Jian Lu, Xiangqun Shuang, Kai Beijing Univ Posts & Telecommun State Key Lab Networking Switching Technol Beijing 100876 PR Peoples R China Yuxi Normal Univ Sch Math & Informat Technol Yuxi 653100 Peoples R China Yunnan Key Lab Smart City & Cyberspace Secur Yuxi Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

ISBN: (纸本)9781450387477

Event extraction is an important information extraction task, aiming at extracting event information from text. Each event consists of triggers and arguments with specific roles. Event extraction methods first identify the trigger and classify it into specific types, and then find out the argument and its role. Traditional methods suffer from scarcity of supervised data. Most existing methods use pre-trained language models to solve this problem. Although these models greatly improved event extraction after fine-tuning, they still need a lot of supervised data to achieve the most advanced downstream task performance. In this work, we propose a simple but effective event extraction model CLEE, which leverages contrastive learning to pre-train language models. Our model first uses a pre-trained model to obtain the context representation of the word, and then uses a contrastive target to reduce the distance between triggers and arguments of the same event, and push the distance between triggers and arguments that are not in an event further. Experiments on ACE 2005 dataset show that CLEE achieves significant improvement.

关键词： Neural network Natural language processing Event extraction Contrastive learning

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A Deep learning-Based System for Document Layout Analysis 2022

A Deep Learning-Based System for Document Layout Analysis

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Hong-Tai Tran Nam-Quan Nguyen Tuan-Anh Tran Xuan-Toan Mai Quoc-Thang Nguyen Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Natl Univ Ho Chi Minh City Linh Trung Ward Ho Chi Minh City Vietnam Cinnamon AI Res Dept Villa 40River View 5Vinhome Golden River2 Ton Ho Chi Minh City Vietnam

ISBN: (纸本)9781450387477

Document image understanding is an essential process in the digital transformation era. Those systems automatically convert a paper document to a digital document for storing and information extracting. In practice, document layout analysis is a critical step for the success of document image modeling. This paper introduces a page segmentation system based on deep neural networks. Our system uses two auto encoder-decoder networks to segment the text-line and non-text components simultaneously. The paragraph segmentation is then realized based on the text line and separator mask. Besides, the non-text elements are also identified. Our algorithm has been tested RDCL2019. Experimental results show that our method is more stable and more comfortable to adapt with a new format layout than the previous commercial and publishing systems.

关键词： Document layout analysis page segmentation OCR Unet document image understanding

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Research on License Plate Recognition Based on Deep learning in Complex Scenarios 23

Research on License Plate Recognition Based on Deep Learning...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Tang, Yinqing Yu, Benguo Liu, Fengning Wang, Anran Hainan Medical University China

ISBN: (纸本)9781450398633

The license plate angle is unfixed, the vehicle position is ununiform, and the picture is not sufficiently illuminated which leads to the decrease of license plate recognition accuracy. In order to improve the accuracy of license plate recognition, a deep learning-based license plate recognition method is proposed. For license plate location problem, YOLOv3 algorithm is used. The algorithm is more capable of recognizing small targets and is suitable for license plate location recognition that requires precise positioning. For the problem of license plate character recognition, the CNN plus multitask recognition method is proposed for recognition. The experimental results show that the accuracy of the license plate recognition method proposed in this paper reaches 96%, and the intelligent license plate recognition is realized. © 2023 ACM.

关键词： License plates (automobile)

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A Hybrid machine learning Method for Diabetes Detection based on Unsupervised Clustering 23

A Hybrid Machine Learning Method for Diabetes Detection base...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Liu, Junhong Peng, Bo Yin, Zezhao School of Computer and Artificial Intelligence Southwest Jiaotong University China

ISBN: (纸本)9781450398633

Diabetes is a common disease, and due to the increasing incidence year by year. But most diabetics can not be easily detected in the early stage, since the symptoms are not obvious. The objective of this study is to propose a machine-learning method based on unsupervised clustering to improve the accuracy of diabetes detection. Due to massive unlabeled data sets and the problems in the traditional K-means clustering algorithms, we adopt the Fuzzy c-means clustering algorithm with an improvement on the calculation of parameter m. Our method includes a combination of the principal component analysis(PCA), an improved Fuzzy c-means (FCM) clustering algorithm, and K-nearest neighbor(KNN) classification algorithm optimized with K value. After 10 times 10-fold cross-validation, the average accuracy of the proposed method reaches 99.31%, which is higher than that of other machine learning models. Therefore, our method is proven to be more suitable for detecting diabetes. At the same time, further experiments on a new data set validate the applicability of our method in a more practical way for the diabetes detection. © 2023 ACM.

关键词： Principal component analysis

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