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Journal articles on the topic 'Multi Expert'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Yang, Lin, Yu-Zhen Janice Chen, Mohammad H. Hajiesmaili, Mark Herbster, and Don Towsley. "Hierarchical Learning Algorithms for Multi-scale Expert Problems." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 2 (2022): 1–29. http://dx.doi.org/10.1145/3530900.

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In this paper, we study the multi-scale expert problem, where the rewards of different experts vary in different reward ranges. The performance of existing algorithms for the multi-scale expert problem degrades linearly proportional to the maximum reward range of any expert or the best expert and does not capture the non-uniform heterogeneity in the reward ranges among experts. In this work, we propose learning algorithms that construct a hierarchical tree structure based on the heterogeneity of the reward range of experts and then determine differentiated learning rates based on the reward up

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2

Lee, Yunsung, JinYoung Kim, Hyojun Go, Myeongho Jeong, Shinhyeok Oh, and Seungtaek Choi. "Multi-Architecture Multi-Expert Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13427–36. http://dx.doi.org/10.1609/aaai.v38i12.29245.

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In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME). We identify the need for tailored operations at different time-steps in diffusion processes and leverage this insight to create compact yet high-performing models. MEME assigns distinct architectures to different time-step intervals, balancing convolution and self-attention operations based on observed frequency characteristics. We also introduce a soft interval assignment strategy for comprehensive training. Empirically, MEME operates 3.3

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Yang, Lin, Yu-zhen Janice Chen, Mohammad H. Hajiesmaili, Mark Herbster, and Don Towsley. "Hierarchical Learning Algorithms for Multi-scale Expert Problems." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 71–72. http://dx.doi.org/10.1145/3547353.3530967.

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In this work,1 we study the multi-scale expert problem, where the rewards of different experts vary in different reward ranges. The performance of existing algorithms for the multi-scale expert problem degrades linearly proportional to the maximum reward range of any expert or the best expert and does not capture the non-uniform heterogeneity in the reward ranges among experts. In this work, we propose learning algorithms that construct a hierarchical tree structure based on the heterogeneity of the reward range of experts and then determine differentiated learning rates based on the reward up

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Wibowo Almais, Agung Teguh, Moechammad Sarosa, and Muhammad Aziz Muslim. "Implementation Of Multi Experts Multi Criteria Decision Making For Rehabilitation And Reconstruction Action After A Disaster." MATICS 8, no. 1 (2016): 27. http://dx.doi.org/10.18860/mat.v8i1.3480.

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<p class="Abstract" style="text-align: justify;"><em>Abstract</em>— This article is aimed to propose a method which used Non-Numeric assessment that is Multi Experts Multi Criteria Decision Making (MEMCDM) in order to construct supporting decision system in composing rehabilitation and reconstruction action after disaster. Because in all this time “Perencanaan dan Pengendalian Penanganan Bencana (P3B)” surveyor team does not have clear standard criteria to execute the compiling rehabilitation and reconstruction action after disaster. Method Multi Experts Multi Criteria Decisi

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Poo, Chiang-Choon Danny, and Hongjun Lu. "Multi-domain expert systems." Expert Systems 8, no. 2 (1991): 67–73. http://dx.doi.org/10.1111/j.1468-0394.1991.tb00254.x.

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Yang, Chuanyu, Kai Yuan, Qiuguo Zhu, Wanming Yu, and Zhibin Li. "Multi-expert learning of adaptive legged locomotion." Science Robotics 5, no. 49 (2020): eabb2174. http://dx.doi.org/10.1126/scirobotics.abb2174.

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Achieving versatile robot locomotion requires motor skills that can adapt to previously unseen situations. We propose a multi-expert learning architecture (MELA) that learns to generate adaptive skills from a group of representative expert skills. During training, MELA is first initialized by a distinct set of pretrained experts, each in a separate deep neural network (DNN). Then, by learning the combination of these DNNs using a gating neural network (GNN), MELA can acquire more specialized experts and transitional skills across various locomotion modes. During runtime, MELA constantly blends

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7

Li, Ming. "An Approach to Expert Finding Based on Multi-Granularity Two-Tuple Linguistic Information." Applied Mechanics and Materials 58-60 (June 2011): 1317–22. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1317.

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Since organizational tacit knowledge resides in the owner’s brain, finding relevant experts in the specific fields is crucial to utilize this type of knowledge. In this paper, an approach to expert finding is proposed to assist the user to find the required experts. The method adopts the multi-granularity two-tuple linguistic information to construct the expert profile, that is, to model expert’s expertise. The user query the expert based on the fuzzy linguistic information. Then, the relevant experts are ranked according to the matching degree between the expert profile and the query. Finally

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Wen, Ta-Chun, Hsin-Hung Lai, and Kuei-Hu Chang. "A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems." Applied Sciences 10, no. 13 (2020): 4582. http://dx.doi.org/10.3390/app10134582.

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Multi-expert multi-criterion decision-making problems are complicated decision issues. Different experts have different opinions with regard to multi-criterion decision-making problems, depending on their background and experience. These experts provide information that usually contains certain, uncertain, and incomplete information simultaneously. However, traditional computing methods only fully consider certain information, and ignore uncertain or incomplete information, which causes information distortion in assessment results. In order to effectively solve the above problems, this paper p

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Beroggi, G. E. G., and W. A. Waliace. "Multi-expert operational risk management." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 30, no. 1 (2000): 32–44. http://dx.doi.org/10.1109/5326.827452.

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Badr, A., Z. Binder, and H. E. Yassin. "Expert Multi-Model Control System." IFAC Proceedings Volumes 22, no. 6 (1989): 243–46. http://dx.doi.org/10.1016/s1474-6670(17)54379-7.

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11

Zheng, Chunjun, Chunli Wang, and Ning Jia. "An Ensemble Model for Multi-Level Speech Emotion Recognition." Applied Sciences 10, no. 1 (2019): 205. http://dx.doi.org/10.3390/app10010205.

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Speech emotion recognition is a challenging and widely examined research topic in the field of speech processing. The accuracy of existing models in speech emotion recognition tasks is not high, and the generalization ability is not strong. Since the feature set and model design of effective speech directly affect the accuracy of speech emotion recognition, research on features and models is important. Because emotional expression is often correlated with the global features, local features, and model design of speech, it is often difficult to find a universal solution for effective speech emo

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Rottoli, Giovanni Daian, and Carlos Casanova. "Multi-criteria and Multi-expert Requirement Prioritization using Fuzzy Linguistic Labels." ParadigmPlus 3, no. 1 (2022): 1–18. http://dx.doi.org/10.55969/paradigmplus.v3n1a1.

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Requirement prioritization in Software Engineering is the activity that helps to select and order for the requirements to be implemented in each software development process iteration. Thus, requirement prioritization assists the decision-making process during iteration management. This work presents a method for requirement prioritization that considers many experts' opinions on multiple decision criteria provided using fuzzy linguistic labels, a tool that allows capturing the imprecision of each experts' judgment. These opinions are then aggregated using the fuzzy aggregation operator MLIOWA

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13

Zhang, Yunhe, Jinyu Cai, Zhihao Wu, Pengyang Wang, and See-Kiong Ng. "Mixture of Experts as Representation Learner for Deep Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22704–13. https://doi.org/10.1609/aaai.v39i21.34430.

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Multi-view clustering (MVC) aims to integrate information from diverse data sources to facilitate the clustering process, which has achieved considerable success in various real-world applications. However, previous MVC methods typically employ one of two strategies: (1) designing separate feature extraction pipelines for each view, which restricts their ability to fully exploit collaborative potential; or (2) employing a single shared representation module, which hinders the capture of diverse, view-specific representations. To tackle these challenges, we introduce Deep Multi-View Clustering

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Nesterenko, Oleksandr, Igor Netesin, Valery Polischuk, and Yuri Selin. "Graph-based decision making for varying complexity multicriteria problems." Computer Science Journal of Moldova 30, no. 3(90) (2022): 391–412. http://dx.doi.org/10.56415/csjm.v30.21.

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In the modern world in various spheres of activity, the number of problems that need multi-criteria decision-making (MCDM) is constantly increasing. Researchers and experts offer a number of approaches to MCDM process in such tasks; in particular, most of them are based on expert methods. However, in practice, these methods require significant intellectual effort of experts and organizational and technical workload during the expert survey, and also usually take a long time. At the same time, it is not always possible for experts to use certain characteristics of alternatives, which also carri

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Smirnov, Evgueni, Hua Zhang, Ralf Peeters, Nikolay Nikolaev, and Maike Imkamp. "Aggregating Human-Expert Opinions for Multi-Label Classification." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 68–69. http://dx.doi.org/10.1609/hcomp.v1i1.13120.

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This paper introduces a multi-label classification problem to the field of human computation. The problem involves training data such that each instance belongs to a set of classes. The true class sets of all the instances are provided together with their estimations presented by m human experts. Given the training data and the class-set estimates of the m experts for a new instance, the multi-label classification problem is to estimate the true class set of that instance. To solve the problem we propose an ensemble approach. Experiments show that the approach can outperform the best expert an

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16

Yang, Li, Xie Dong Cao, and Jie Li. "Research on Minimum-Cost-Based Task Decomposition Model of Multiple Expert Systems for Oil-Gas Reservoir Protection Based on Agent." Applied Mechanics and Materials 275-277 (January 2013): 2650–53. http://dx.doi.org/10.4028/www.scientific.net/amm.275-277.2650.

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To solve collaboration problem in multi-expert system, intelligent agent technology is used in multi-expert system.Firstly,by introducing a minimum-cost-based formal description of task decomposition,a new agent task decompositon model is presented.Secondly, heuristic algorithm for task decomposition is analyzed to solve communication among agents.For illustration, expert systems for oil-gas reservoir protection is utilized to verify the effectiveness of the method.The application of the expert system shows that based-minimum-cost task decompostion model is valid in agent task decomposition an

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17

Wagner, Matthias, Gwendolin Gurr, and Miriam Siemon. "Voices in health communication — experts and expert-roles in the German news coverage of multi resistant pathogens." Journal of Science Communication 18, no. 06 (2019): A03. http://dx.doi.org/10.22323/2.18060203.

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When it comes to complex topics in the field of health and risk communication, experts are of high importance for the credibility of a news media report. This paper examines the use of experts and their roles in the news media coverage of multi-resistant pathogens by means of a quantitative content analysis of German print and online news. A cluster analysis of the expert statements identifies three different statement frames describing different expert roles. The results show manifest patterns of selected expert sources, which points to professionalized mechanisms of selecting expert sources

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18

Du, Xiuli, Kun Lu, Rui Zhou, Yana Lv, and Shaoming Qiu. "A Weighting Method Based on the Improved Hesitation of Pythagorean Fuzzy Sets." Electronics 12, no. 13 (2023): 3001. http://dx.doi.org/10.3390/electronics12133001.

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The existing expert weight determination method for multi-attribute decision making based on the Pythagorean fuzzy number approach does not make sufficient use of the hesitation involved with the decision information, which may cause biased weight assignment. Therefore, to address the issue of unknown expert weights and attribute evaluation based on Pythagorean fuzzy numbers in multi-attribute group decision-making problems, a weight determination method is proposed that improves the treatment of hesitation in Pythagorean fuzzy sets. Firstly, the proximity of experts and similarity of the modi

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19

Grujčić, Željko, and Brankica Pažun. "An example of multi-criteria optimization using Expert Choice." Serbian Journal of Engineering Management 4, no. 2 (2019): 18–28. http://dx.doi.org/10.5937/sjem1902018g.

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20

Zhang, Gong-Duo, Ruiqing Chen, Qian Zhao, et al. "Bagging-Expert Network for Multi-Task Learning: A Depolarization Solution in Multi-Gate Mixture-of-Experts." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22389–97. https://doi.org/10.1609/aaai.v39i21.34395.

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Multi-task learning (MTL) is widely utilized across a variety of real-world applications, including recommendation systems. For instance, in the field of e-commerce, MTL is commonly employed to simultaneously model click, conversion, and user dwelling time. Among a various of MTL models, the Multi-gate Mixture-of-Experts (MMoE) has gained significant popularity. However, MMoE suffers from the polarization issue during training, where the weights of certain experts tend to converge towards 0. To address this issue, we propose a novel method called Bagging-Expert network (BEnet) for multi-task l

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21

Zheng, Zhuobin, Chun Yuan, Xinrui Zhu, et al. "Self-Supervised Mixture-of-Experts by Uncertainty Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5933–40. http://dx.doi.org/10.1609/aaai.v33i01.33015933.

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Learning related tasks in various domains and transferring exploited knowledge to new situations is a significant challenge in Reinforcement Learning (RL). However, most RL algorithms are data inefficient and fail to generalize in complex environments, limiting their adaptability and applicability in multi-task scenarios. In this paper, we propose SelfSupervised Mixture-of-Experts (SUM), an effective algorithm driven by predictive uncertainty estimation for multitask RL. SUM utilizes a multi-head agent with shared parameters as experts to learn a series of related tasks simultaneously by Deep

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22

Yu, Guodong, Li Zhang, and Huiping Sun. "A Method for Partner Selection of Supply Chain Using Interval-Valued Fuzzy Sets — Fuzzy Choquet Integral and Improved Dempster–Shafer Theory." International Journal of Information Technology & Decision Making 17, no. 06 (2018): 1777–804. http://dx.doi.org/10.1142/s0219622018500438.

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Supply chain partner selection (SCPS) is an important issue for firms to remain highly competitive. SCPS is generally formulated as a fuzzy multi-criteria decision-making problem, of which the most crucial step is how to cope with the fuzzy correlations of multi-criteria and individual differences of experts. To gain more exact results, we adopt interval-valued intuitionistic fuzzy sets to express the fuzziness of multi-criteria. Fuzzy Choquet integral model is developed to explain correlations of multi-criteria and calculate the evaluation results of each expert. An improved Dempster–Shafer t

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23

Ratcliffe, Sarah, Sheryl Potashnik, Maxim Topaz, et al. "Using Electronic Case Summaries to Elicit Multi-Disciplinary Expert Knowledge about Referrals to Post-Acute Care." Applied Clinical Informatics 07, no. 02 (2016): 368–79. http://dx.doi.org/10.4338/aci-2015-11-ra-0161.

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SummaryEliciting knowledge from geographically dispersed experts given their time and scheduling constraints, while maintaining anonymity among them, presents multiple challenges.Describe an innovative, Internet based method to acquire knowledge from experts regarding patients who need post-acute referrals. Compare, 1) the percentage of patients referred by experts to percentage of patients actually referred by hospital clinicians, 2) experts’ referral decisions by disciplines and geographic regions, and 3) most common factors deemed important by discipline.De-identified case studies, develope

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Beskese, Ahmet, Cengiz Kahraman, Shirli Ender Buyukbay, and Faik Tunc Bozbura. "AN INTIUTIONISTIC FUZZY MULTI-EXPERT AND MULTI-CRITERIA SYSTEM FOR EFFECTIVE PERFORMANCE MANAGEMENT." Technological and Economic Development of Economy 24, no. 6 (2018): 2179–201. http://dx.doi.org/10.3846/tede.2018.6462.

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Organizations, in their pursuit of accomplishing their vision and goals, need effective management of human resources. Performance Management, among other Human Resources Management (HRM) practices, is the central function, as it delivers the necessary data that complements and enables the other functions. Building an effective performance management system is a multicriteria problem that requires contribution from experts having diverse backgrounds. Moreover, performance management is an inherently vague concept since almost the whole process requires linguistic assessments rather than numeri

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Beskese, Ahmet, Cengiz Kahraman, Shirli Ender Buyukbay, and Faik Tunc Bozbura. "AN INTIUTIONISTIC FUZZY MULTI-EXPERT AND MULTI-CRITERIA SYSTEM FOR EFFECTIVE PERFORMANCE MANAGEMENT." Technological and Economic Development of Economy 24, no. 6 (2018): 2179–201. http://dx.doi.org/10.3846/10.3846/tede.2018.6462.

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Organizations, in their pursuit of accomplishing their vision and goals, need effective management of human resources. Performance Management, among other Human Resources Management (HRM) practices, is the central function, as it delivers the necessary data that complements and enables the other functions. Building an effective performance management system is a multicriteria problem that requires contribution from experts having diverse backgrounds. Moreover, performance management is an inherently vague concept since almost the whole process requires linguistic assessments rather than numeri

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Zhu, Ying, Pengjun Wang, Jiayan Zhuang, Yi Zhu, Jiangjian Xiao, and Xiong Oyang. "Multi-Mode Multi-Feature Joint Intelligent Identification Methods for Nematodes." Applied Sciences 13, no. 13 (2023): 7583. http://dx.doi.org/10.3390/app13137583.

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The identification of plant nematodes is crucial in the fields of pest control, soil ecology, and biogeography. The automated recognition of plant nematodes based on deep-learning technology can significantly improve the accuracy and efficiency of their recognition. In this study, we devised a method for the multi-mode, multi-feature identification of plant nematodes using deep-learning techniques which emulated the recognition logic of domain experts. Beginning with a multi-featured plant nematode dataset, we not only designed key feature extraction strategies to address the problem of weak k

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Chen, Weijun, and Yanze Wang. "DHMoE: Diffusion Generated Hierarchical Multi-Granular Expertise for Stock Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11490–99. https://doi.org/10.1609/aaai.v39i11.33250.

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Stock prediction stands as a pivotal research objective within the Fintech. Existing deep learning research revolves around the development and scaling of one individual neural network predictor. However, in the dynamic and noisy landscape of the stock market, reliance solely on a single predictor poses risks of limited adaptability to diverse market conditions and challenges in effectively integrating multi-source information. Besides, top-down teaching and bottom-up hierarchical decision-making paradigms are critical for robust and accurate stock prediction within successful quantitative fir

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Pronichkin, S. V., Y. S. Chernyshova, A. V. Kholstov, and I. B. Mamai. "Multiplicity based algorithms for processing group multi-criteria expert assessments." Journal of Physics: Conference Series 2060, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/2060/1/012010.

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Abstract The paper discusses a new approach to the processing of verbal assessments, which express the opinions of experts on many criteria. The algorithm for verbal analysis of multi-feature objects expert assessments is proposed for identifying subgroups of experts with agreed opinions. It is proposed to use the multiplicity indices of multisets to calculate the coefficient of consistency. It is also proposed to use jointly the coefficients of consistency values to assess the consistency of the experts subgroups on one feature, and strive for the total maximization of the coefficients of con

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Okesola, Julius O., Oluwafemi S. Ogunseye, and Olusegun Folorunso. "An Efficient Multi-Expert Knowledge Capture Technique." International Journal of Computer Applications 8, no. 10 (2010): 6–9. http://dx.doi.org/10.5120/1245-1611.

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Li, Zhetao, Wei Wei, Tianzhu Zhang, Meng Wang, Sujuan Hou, and Xin Peng. "Online Multi-Expert Learning for Visual Tracking." IEEE Transactions on Image Processing 29 (2020): 934–46. http://dx.doi.org/10.1109/tip.2019.2931082.

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Verlinde, Patrick, Gérard Chollet, and Marc Acheroy. "Multi-modal identity verification using expert fusion." Information Fusion 1, no. 1 (2000): 17–33. http://dx.doi.org/10.1016/s1566-2535(00)00002-6.

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Nurbekova, Zhanat, Talgat Sembayev, Dauren Dossymbek, Kanagat Baigusheva, Sayat Omarov, and Yeldos Nurlanov. "A Multi-criteria-Based Expert Modeling for Assessing the Textbook Quality." International Journal of Information and Education Technology 13, no. 9 (2023): 1408–21. http://dx.doi.org/10.18178/ijiet.2023.13.9.1944.

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This research considers the application of a multi-criteria approach to assess the quality of textbook content. The issues of determining evaluation criteria and organizing the work of experts are brought to the fore, as these factors directly affect the objectivity and efficiency of the evaluation process. The definition of criteria consisted of reviewing common mistakes and comments on the quality of textbooks, forming a preliminary and final list of criteria. The organization of the work of the experts consisted in the development of a platform with the introduction of a multi-criteria appr

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Nie, Song, Huchang Liao, Xingli Wu, Ming Tang, and Abdullah Al-Barakati. "HESITANT FUZZY LINGUISTIC DNMA METHOD WITH CARDINAL CONSENSUS REACHING PROCESS FOR SHOPPING MALL LOCATION SELECTION." International Journal of Strategic Property Management 23, no. 6 (2019): 420–34. http://dx.doi.org/10.3846/ijspm.2019.10851.

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The hesitant fuzzy linguistic term set is an effective tool to express qualitative evaluations since it is close to human reasoning and expressing habits. In this paper, we propose a multi-expert multi-criterion decision-making method integrating the double normalization-based multi-aggregation (DNMA) method with a cardinal consensus reaching process, where the assessments of alternatives over multiple criteria are expressed as hesitant fuzzy linguistic term sets. To do so, the DNMA method involving double normalizations and three aggregation tools is extended to deal with the hesitant fuzzy l

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Luo, Haisen. "Gated Multi-task Learning for QoS-aware Service Recommendation." Journal of Physics: Conference Series 2547, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2547/1/012011.

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Abstract In the ever-increasing diversity of cloud environment, the recommendation of web service-related applications based on service quality is a basic tasks that both service providers and web developers are very interested in. A key challenge is how to get personalized and accurate QoS values of web services from the multi-source information of users/services. In addition, complex contexts and multi-tasking attributes also make the collaborative services quality prediction difficult to meet. Inspired by the progress of multi-task deep learning, this paper proposes a new framework of servi

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Liang, Mengquan, Xuedong Wu, Yaonan Wang, Zhiyu Zhu, Baiheng Cao, and Jie Xu. "Multi-Model and Multi-Expert Correlation Filter for High-Speed Tracking." IEEE Access 9 (2021): 52326–35. http://dx.doi.org/10.1109/access.2021.3069786.

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Noor-E-Alam, Md, Tahmina Ferdousi Lipi, M. Ahsan Akhtar Hasin, and A. M. M. S. Ullah. "Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)." Knowledge-Based Systems 24, no. 3 (2011): 367–77. http://dx.doi.org/10.1016/j.knosys.2010.10.006.

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Sprinz, Detlef F., Bruce Bueno de Mesquita, Steffen Kallbekken, Frans Stokman, Håkon Sælen, and Robert Thomson. "Predicting Paris: Multi-Method Approaches to Forecast the Outcomes of Global Climate Negotiations." Politics and Governance 4, no. 3 (2016): 172–87. http://dx.doi.org/10.17645/pag.v4i3.654.

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We examine the negotiations held under the auspices of the United Nations Framework Convention of Climate Change in Paris, December 2015. Prior to these negotiations, there was considerable uncertainty about whether an agreement would be reached, particularly given that the world’s leaders failed to do so in the 2009 negotiations held in Copenhagen. Amid this uncertainty, we applied three different methods to predict the outcomes: an expert survey and two negotiation simulation models, namely the Exchange Model and the Predictioneer’s Game. After the event, these predictions were assessed agai

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Jalali, Hamed, and Gjergji Kasneci. "Multilabel Classification for Entry-Dependent Expert Selection in Distributed Gaussian Processes." Entropy 27, no. 3 (2025): 307. https://doi.org/10.3390/e27030307.

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By distributing the training process, local approximation reduces the cost of the standard Gaussian process. An ensemble method aggregates predictions from local Gaussian experts, each trained on different data partitions, under the assumption of perfect diversity among them. While this assumption ensures tractable aggregation, it is frequently violated in practice. Although ensemble methods provide consistent results by modeling dependencies among experts, they incur a high computational cost, scaling cubically with the number of experts. Implementing an expert-selection strategy reduces the

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Zhang, Rongyu, Yulin Luo, Jiaming Liu, et al. "Efficient Deweahter Mixture-of-Experts with Uncertainty-Aware Feature-Wise Linear Modulation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16812–20. http://dx.doi.org/10.1609/aaai.v38i15.29622.

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The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adverse weather effects. However, the conventional MoE architecture with parallel Feed Forward Network (FFN) experts leads to significant parameter and computational overheads that hinder its efficient deployment. In addition, the naive MoE linear router is suboptimal in assigning task-specific features to multiple experts which limits its further scalability. In this work, we propose an efficient MoE architecture with w

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Rowley, Kurt. "Inquiry into the Practices of Expert Courseware Designers: A Pragmatic Method for the Design of Effective Instructional Systems." Journal of Educational Computing Research 33, no. 4 (2005): 419–50. http://dx.doi.org/10.2190/9mlr-artq-bd1p-ketn.

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A multi-stage study of the practices of expert courseware designers was conducted with the final goal of identifying methods for assisting non-experts with the design of effective instructional systems. A total of 25 expert designers were involved in all stages of the inquiry. A model of the expert courseware design process was created, tested, and refined through four try-outs. The final version of the model included instructional design task descriptions and electronic worksheets. The study revealed a common expert process that included the use of rapid prototypes, an opportunistic method of

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Yang, Li, and Hao De Liao. "Research on Cooperation Mechanism of Multiple Expert Systems for Oil-Gas Reservoir Protection Based on Agent." Applied Mechanics and Materials 312 (February 2013): 835–39. http://dx.doi.org/10.4028/www.scientific.net/amm.312.835.

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To solve collaboration problem in intelligent agents, a new cooperation mechanism is used in multi-expert system.Firstly,by introducing five-tuple with constraint condition,a new agent task decomposition model is presented.Secondly, blackboard mechanism based on point-to point is analyzed to solve communication among agents. The application of the expert system shows that our proposed task decompostion model is valid and blackboard mechanism based on point-to point can be used as communication among agents.Finally, minimum-cost-based cooperation mechanism based on agents can effectively solve

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Xu, Xiaoyue, Danhui Fang, and Yinghua Song. "Emergency decision-making method based on multi-attribute intuitionistic fuzzy and evidence theory." E3S Web of Conferences 253 (2021): 02027. http://dx.doi.org/10.1051/e3sconf/202125302027.

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Multi-attribute emergency decision-making problems have the characteristics of incomplete information and shortage of response time. Evidence theory can effectively express uncertain information in the decision-making process. However, evidence theory requires that the condition of evidence independence is met, and the evaluation information of experts is often vague. Therefore, an emergency decision-making method based on intuitionistic fuzzy sets and evidence theory is proposed. First, each expert gives an intuitionistic fuzzy evaluation of each emergency plan. Secondly, the proposed intuiti

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Kedong, Yin, Shiwei Zhou, and Tongtong Xu. "Research on optimization of index system design and its inspection method." Marine Economics and Management 2, no. 1 (2019): 1–28. http://dx.doi.org/10.1108/maem-10-2019-0010.

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Purpose To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness. Design/meth

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Lee, Hyunji, Luca Soldaini, Arman Cohan, Minjoon Seo, and Kyle Lo. "RouterRetriever: Routing over a Mixture of Expert Embedding Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11995–2003. https://doi.org/10.1609/aaai.v39i11.33306.

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Information retrieval methods often rely on a single embedding model trained on large, general-domain datasets like MSMARCO. While this approach can produce a retriever with reasonable overall performance, they often underperform models trained on domain-specific data when testing on their respective domains. Prior work in information retrieval has tackled this through multi-task training, but the idea of routing over a mixture of domain-specific expert retrievers remains unexplored despite the popularity of such ideas in language model generation research. In this work, we introduce RouterRet

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Almarzoqi, Samar Adel, Ahmed Yahya, Zaki Matar, and Ibrahim Gomaa. "Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance." Sensors 22, no. 4 (2022): 1603. http://dx.doi.org/10.3390/s22041603.

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Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work’s focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution. The contribution of this paper is to study the effect of the re-learning EXP3 Multi-Armed Bandit (MAB) algorithm with previous experts’ advice on the LoRaWAN network performance. LoRa smart node has a self-managed EXP3 algorithm for choosing and updating the transmission parameters based on its observation. The best parameter choice needs previously associat

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Sirbiladze, Gia, Koba Gelashvili, Irina Khutsishvili, and Anna Sikharulidze. "Temporalized Structure of Bodies of Evidence in the Multi-Criteria Decision-Making Model." International Journal of Information Technology & Decision Making 14, no. 03 (2015): 565–96. http://dx.doi.org/10.1142/s021962201550008x.

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In this paper, we perform the analysis of temporalized structure of bodies of evidence to construct more precise decisions based on the mathematical model of experts’ evaluations. The relation of information precision is defined on a monotone sequence of the bodies of evidence. For determining of a body of evidence the maximum principles of nonspecificity measure, the Shannon and Shapley entropies are applied. Corresponding mathematical programming problems are constructed. A new approach for the numerical solution of these problems is developed. The temporalized structure of bodies of evidenc

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Hadi, Wa'el. "EMCAR: Expert Multi Class Based on Association Rule." International Journal of Modern Education and Computer Science 5, no. 3 (2013): 33–41. http://dx.doi.org/10.5815/ijmecs.2013.03.05.

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Akçura, M. Tolga, and Zafer D. Ozdemir. "A Strategic Analysis of Multi-Channel Expert Services." Journal of Management Information Systems 34, no. 1 (2017): 206–31. http://dx.doi.org/10.1080/07421222.2017.1297637.

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NAMATAME, Akira, and Youich OHSAWA. "Neuro-Expert Systems with Structured Multi-Layered Networks." Transactions of the Institute of Systems, Control and Information Engineers 5, no. 9 (1992): 380–89. http://dx.doi.org/10.5687/iscie.5.380.

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Huang, Lin-Lin, and Akinobu Shimizu. "A multi-expert approach for robust face detection." Pattern Recognition 39, no. 9 (2006): 1695–703. http://dx.doi.org/10.1016/j.patcog.2005.11.020.

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