Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction最新文献

Proceedings of the 5th International Conference on Machine Learning and Machine Intelligence, MLMI 2022, Hangzhou, China, September 23-25, 2022 第五届机器学习与机器智能国际会议论文集，MLMI 2022，杭州，中国，2022年9月23日至25日

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2022-01-01 DOI: 10.1145/3568199

引用次数: 0

Text Categorization of Filipino Tweets Using Naïve Byes Algorithm 使用Naïve Byes算法对菲律宾文推文进行文本分类

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2021-01-01 DOI: 10.1145/3490725.3490732

Sharmaine Justyne Ramos Maglapuz, L. L. Lacatan

引用次数: 0

Sentiment Analysis of Facebook Posts Towards Good Governance Using SVM Algorithm: A Framework Proposal 基于SVM算法的Facebook帖子对善治的情感分析:一个框架建议

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2021-01-01 DOI: 10.1145/3490725.3490746

Regina Garcia Almonte, L. L. Lacatan

引用次数: 0

Coompetency-Based Mapping Tool in Personnel Management System using Analytical Hierarchy Process 基于层次分析法的人事管理系统中能力映射工具

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2021-01-01 DOI: 10.1145/3490725.3490734

L. L. Lacatan, Gary Mendoza Penuliar

引用次数: 0

IoT and RS Techniques for Enhancing Water Use Efficiency and Achieving Water Security 提高用水效率和实现水安全的物联网和遥感技术

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2021-01-01 DOI: 10.1145/3490725.3490738

Y. Al-Mulla, Taif B. Al-Badi

引用次数: 0

Swarm AGV Optimization Using Deep Reinforcement Learning 基于深度强化学习的群AGV优化

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426839

Pilar Arques-Corrales, F. A. Gregori

{"title":"Swarm AGV Optimization Using Deep Reinforcement Learning","authors":"Pilar Arques-Corrales, F. A. Gregori","doi":"10.1145/3426826.3426839","DOIUrl":"https://doi.org/10.1145/3426826.3426839","url":null,"abstract":"Behavior design for Automated Guided Vehicles (AGV) systems is an active research area, fundamental for robotics, industrial systems automation. The rise of machine learning neural systems and deep learning make promising results in a multitude of areas including warehouse environments.In this paper, several different policies will be obtained by using reinforcement learning on a heterogeneous swarm robotic system, applied for solving logistical tasks in Automated Guided Vehicles. More specifically, two different types of agents will be used: the vehicles that collect, transport and deposit their package and the traffic lights that regulate the number of vehicles that circulate on the tracks. The main objective of our work is to learn simultaneously two different control policies, one for each kind of agent.The obtained policies have shown their ability to correctly learn the package transport behavior in addition to balance traffic flow to facilitate agent mobility and avoid collisions. Furthermore, the scalability of the system and the behavior performance for different number of vehicles has been shown.","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"21 1","pages":"65-69"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88154630","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: 1

Comparison of Evolutionary Strategies for Reinforcement Learning in a Swarm Aggregation Behaviour 群聚集行为中强化学习的进化策略比较

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2020-01-01 DOI: 10.1145/3426826.3426835

Jasmina Rais Martínez, F. A. Gregori

引用次数: 1

Proceedings of the 2nd International Conference on Machine Learning and Machine Intelligence, MLMI 2019, Jakarta, Indonesia, September 18-20, 2019 第二届机器学习与机器智能国际会议论文集，MLMI 2019，印度尼西亚雅加达，2019年9月18-20日

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2019-09-18 DOI: 10.1145/3366750

引用次数: 0

Modeling and Feature Analysis of Air Traffic Management Technical Support System Based on Weighted Complex Network 基于加权复杂网络的空中交通管理技术支持系统建模与特征分析

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2019-01-01 DOI: 10.1145/3366750.3366763

Jiayu Quan, Songchen Han, Peng Li, Binbin Liang, Lisha Yu, Kunshan Yang

引用次数: 0

A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling. 一点说明很多：一种通过一点标记的MR前列腺交互式分割方法。

Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction Pub Date : 2017-01-01 Epub Date: 2017-09-07 DOI: 10.1007/978-3-319-67389-9_26

Jinquan Sun, Yinghuan Shi, Yang Gao, Dinggang Shen

{"title":"A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling.","authors":"Jinquan Sun, Yinghuan Shi, Yang Gao, Dinggang Shen","doi":"10.1007/978-3-319-67389-9_26","DOIUrl":"10.1007/978-3-319-67389-9_26","url":null,"abstract":"<p><p>In this paper, we investigate if the MR prostate segmentation performance could be improved, by only providing one-point labeling information in the prostate region. To achieve this goal, by asking the physician to first click one point inside the prostate region, we present a novel segmentation method by simultaneously integrating the boundary detection results and the patch-based prediction. Particularly, since the clicked point belongs to the prostate, we first generate the location-prior maps, with two basic assumptions: (1) a point closer to the clicked point should be with higher probability to be the prostate voxel, (2) a point separated by more boundaries to the clicked point, will have lower chance to be the prostate voxel. We perform the Canny edge detector and obtain two location-prior maps from horizontal and vertical directions, respectively. Then, the obtained location-prior maps along with the original MR images are fed into a multi-channel fully convolutional network to conduct the patch-based prediction. With the obtained prostate-likelihood map, we employ a level-set method to achieve the final segmentation. We evaluate the performance of our method on 22 MR images collected from 22 different patients, with the manual delineation provided as the ground truth for evaluation. The experimental results not only show the promising performance of our method but also demonstrate the one-point labeling could largely enhance the results when a pure patch-based prediction fails.</p>","PeriodicalId":90643,"journal":{"name":"Machine learning for multimodal interaction : ... international workshop, MLMI ... : revised selected papers. Workshop on Machine Learning for Multimodal Interaction","volume":"10541 ","pages":"220-228"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-67389-9_26","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36647955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5