Academic literature on the topic 'Maximum consensus'
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Journal articles on the topic "Maximum consensus"
Wang, Xin, Jianping He, Peng Cheng, and Jiming Chen. "Differentially Private Maximum Consensus." IFAC-PapersOnLine 50, no. 1 (July 2017): 9509–14. http://dx.doi.org/10.1016/j.ifacol.2017.08.1597.
Full textAßfalg, André, and Edgar Erdfelder. "CAML—Maximum likelihood consensus analysis." Behavior Research Methods 44, no. 1 (July 27, 2011): 189–201. http://dx.doi.org/10.3758/s13428-011-0138-0.
Full textVenkategowda, Naveen K. D., and Stefan Werner. "Privacy-Preserving Distributed Maximum Consensus." IEEE Signal Processing Letters 27 (2020): 1839–43. http://dx.doi.org/10.1109/lsp.2020.3029706.
Full textWiedermann, Gerhard. "Maximum Immunization for Travel: Consensus." Journal of Travel Medicine 2, no. 3 (September 1, 1995): 191–92. http://dx.doi.org/10.1111/j.1708-8305.1995.tb00652.x.
Full textAxmann, J., and C. Brenner. "MAXIMUM CONSENSUS LOCALIZATION USING LIDAR SENSORS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021 (June 17, 2021): 9–16. http://dx.doi.org/10.5194/isprs-annals-v-2-2021-9-2021.
Full textNakamura, Masahiro, Hideaki Ishii, and Seyed Mehran Dibaji. "Maximum-Based Consensus and Its Resiliency." IFAC-PapersOnLine 51, no. 23 (2018): 283–88. http://dx.doi.org/10.1016/j.ifacol.2018.12.049.
Full textWen, Fei, Rendong Ying, Zheng Gong, and Peilin Liu. "Efficient Algorithms for Maximum Consensus Robust Fitting." IEEE Transactions on Robotics 36, no. 1 (February 2020): 92–106. http://dx.doi.org/10.1109/tro.2019.2943061.
Full textChin, Tat-Jun, and David Suter. "The Maximum Consensus Problem: Recent Algorithmic Advances." Synthesis Lectures on Computer Vision 7, no. 2 (February 27, 2017): 1–194. http://dx.doi.org/10.2200/s00757ed1v01y201702cov011.
Full textNakamura, Masahiro, Hideaki Ishii, and Seyed Mehran Dibaji. "Resiliency against malicious agents in maximum-based consensus." SICE Journal of Control, Measurement, and System Integration 14, no. 1 (January 1, 2021): 279–90. http://dx.doi.org/10.1080/18824889.2021.1988396.
Full textEDDY, SEAN R., GRAEME MITCHISON, and RICHARD DURBIN. "Maximum Discrimination Hidden Markov Models of Sequence Consensus." Journal of Computational Biology 2, no. 1 (January 1995): 9–23. http://dx.doi.org/10.1089/cmb.1995.2.9.
Full textDissertations / Theses on the topic "Maximum consensus"
Sampath, Srinath. "Analysis of Agreement Between Two Long Ranked Lists." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385415346.
Full textTruong, Ha-Giang. "Robust fitting: Assisted by semantic analysis and reinforcement learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2567.
Full textWhipps, Gene Thomas. "Contributions to Distributed Detection and Estimation over Sensor Networks." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1502970194073045.
Full textIzza, Yacine. "Informatique ubiquitaire : techniques de curage d'informations perverties On the extraction of one maximal information subset that does not conflit with multiple contexts Extraction d'un sous-ensemble maximal qui soit cohérent avec des contextes mutuellement contradictoires On computing one max-inclusion consensus On admissible consensuses Boosting MCSes enumeration." Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0405.
Full textThis thesis studies a possible approach of artificial intelligence for detecting and filtering inconsistent information in knowledge bases of intelligent objects and components in ubiquitous computing. This approach is addressed from a practical point of view in the SAT framework;it is about implementing a techniques of filtering inconsistencies in contradictory bases. Several contributions are made in this thesis. Firstly, we have worked on the extraction of one maximal information set that must be satisfiable with multiple assumptive contexts. We have proposed an incremental approach for computing such a set (AC-MSS). Secondly, we were interested about the enumeration of maximal satisfiable sets (MSS) or their complementary minimal correction sets (MCS) of an unsatisfiable CNF instance. In this contribution, a technique is introduced that boosts the currently most efficient practical approaches to enumerate MCS. It implements a model rotation paradigm that allows the set of MCS to be computed in an heuristically efficient way. Finally, we have studied a notion of consensus to reconcile several sources of information. This form of consensus can obey various preference criteria, including maximality one. We have then developed an incremental algorithm for computing one maximal consensus with respect to set-theoretical inclusion. We have also introduced and studied the concept of admissible consensus that refines the initial concept of consensus
Cheng, Li-chen, and 鄭麗珍. "Mining maximum consensus sequences from group ranking data." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/58303646266345835813.
Full text國立中央大學
資訊管理研究所
96
In the last decade, the problem of getting a consensus group ranking from users’ ranking data has received increased attention due to its widespread applications. Previous research solved this problem by consolidating the opinions of all users, thereby obtaining an ordering list of all items that represent the achieved consensus. The weakness of this approach, however, is that it always produces a ranking list of all items, regardless of how many conflicts exist among users. This work rejects the forced agreement of all items. Instead, we define a new concept, maximum consensus sequences, which are the longest ranking lists of items that agree with the majority and disagree only with the minority. Based on this concept, we use two kinds of input data, individual’s total ranking and individual’s partial rankings, to develop algorithms to discover maximum consensus sequences and also to identify conflict items that need further negotiation. Besides, we propose another algorithm to achieve personalized rankling list which can be used in recommender system. Extensive experiments are carried out using synthetic data sets, and the results indicate that the proposed methods are computationally efficient.
Le, Huu Minh. "New algorithmic developments in maximum consensus robust fitting." Thesis, 2018. http://hdl.handle.net/2440/115183.
Full textThesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 2018
Doung, Ming-Chang, and 董佲昌. "An Exact Algorithm for Constructing a Maximum Consensus tree from Rooted Triples." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/98998582712411857862.
Full text國立中正大學
資訊工程研究所
92
In this thesis, we study the important problem that combines the evolutionary trees on overlapping leaf sets into one tree in computational biology. But the input trees are hard to consistent. So there is an optimization problem about consensus tree. Then we give a branch-and-bound algorithm to find a maximum consensus tree from rooted triple. We implement the algorithm and test it to prove our algorithm better than the other algorithms in some situation.
Tsai, Pei-Chun, and 蔡佩君. "An Improved Exact Algorithm for Constructing a Maximum Consensus Tree from Rooted Triples." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/49372232669428053043.
Full text國立中正大學
資訊工程所
93
The maximum consensus tree problem is to find a rooted binary tree that can satisfy the input triples as many as possible. In this thesis, we use a branch-and-bound algorithm with the persistent data structure to solve the maximum consensus tree problem. Using the persistent data structure, we can enhance the running time for branching and use less memory space to save each subproblem. Due to these two properties, the total running time is better, and we can deal with instances with more species.
Hsu, Chih-Cheng, and 許志成. "Genetic Algorithms for Constructing multiple Consensus Evolutionary Trees Using Bootstrapping and Maximum Likelihood Criterion." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/d9kmj8.
Full text銘傳大學
資訊工程學系碩士班
92
Phylogeny analysis is a process which derives the branching and mutations happened during the evolution. There are two main phylogeny analysis methods: distance-based and character-based; and there are two types of evolutionary trees: rooted and unrooted. This paper presents a maximum criterion-based phylogeny analysis method for unrooted trees. To increase the confidence level of the derived evolutionary trees, we use bootstrapping and consensus analysis in conjunction with a genetic algorithm for crossover and mutation between evolutionary trees. Multiple datasets of DNA sequences of species are generated using bootstrapping, the evolutionary trees are evaluated using these datasets and evolve to optimal trees by a genetic algorithm. The evolutionary trees are clustered based on similarity, a consensus tree is produced for each cluster. The experimental results manifest that the consensus trees produced by our system are highly similar to those validated by biologists.
Books on the topic "Maximum consensus"
Chin, Tat-Jun, and David Suter. The Maximum Consensus Problem. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-031-01818-3.
Full textSuter, David, Gerard Medioni, Sven Dickinson, and Tat-Jun Chin. Maximum Consensus Problem: Recent Algorithmic Advances. Morgan & Claypool Publishers, 2017.
Find full textSuter, David, Gerard Medioni, Sven Dickinson, and Tat-Jun Chin. Maximum Consensus Problem: Recent Algorithmic Advances. Morgan & Claypool Publishers, 2017.
Find full textSuter, David, and Tat-Jun Chin. Maximum Consensus Problem: Recent Algorithmic Advances. Springer International Publishing AG, 2017.
Find full textSchake, Kori. The National Security Process. Edited by Derek S. Reveron, Nikolas K. Gvosdev, and John A. Cloud. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190680015.013.12.
Full textBerrill, Andrew, and Pawan Gupta. General principles of regional anaesthesia. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0052.
Full textDietz, Volker, and Nick S. Ward, eds. Oxford Textbook of Neurorehabilitation. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198824954.001.0001.
Full textRatel, Sébastien, and Craig A. Williams. Neuromuscular fatigue. Edited by Neil Armstrong and Willem van Mechelen. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198757672.003.0009.
Full textBook chapters on the topic "Maximum consensus"
Chin, Tat-Jun, and David Suter. "The Maximum Consensus Problem." In The Maximum Consensus Problem, 1–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_1.
Full textChin, Tat-Jun, and David Suter. "Preprocessing for Maximum Consensus." In The Maximum Consensus Problem, 127–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_4.
Full textChin, Tat-Jun, and David Suter. "Exact Algorithms." In The Maximum Consensus Problem, 81–126. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_3.
Full textChin, Tat-Jun, and David Suter. "Approximate Algorithms." In The Maximum Consensus Problem, 21–79. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01818-3_2.
Full textSung, Wing-Kin. "Greedy Consensus Tree and Maximum Greedy Consensus Tree Problems." In WALCOM: Algorithms and Computation, 305–16. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-10564-8_24.
Full textZhang, Liang, Houman Rastgar, Demin Wang, and André Vincent. "Maximum Likelihood Estimation Sample Consensus with Validation of Individual Correspondences." In Advances in Visual Computing, 447–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10331-5_42.
Full textPurkait, Pulak, Christopher Zach, and Anders Eriksson. "Maximum Consensus Parameter Estimation by Reweighted $$\ell _1$$ ℓ 1 Methods." In Lecture Notes in Computer Science, 312–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78199-0_21.
Full textKenyeres, Martin, and Jozef Kenyeres. "How to Optimally Reconfigure Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs." In Software Engineering Application in Systems Design, 189–204. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21435-6_16.
Full textTheisler, Charles. "Consents and Releases." In Maximum Malpractice Protection, 127–46. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003352464-11.
Full textTorra, Vicenç, Guillermo Navarro-Arribas, and Edgar Galván. "Explaining Recurrent Machine Learning Models: Integral Privacy Revisited." In Privacy in Statistical Databases, 62–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57521-2_5.
Full textConference papers on the topic "Maximum consensus"
Duan, Xiaoming, Jianping He, Peng Cheng, Yilin Mo, and Jiming Chen. "Privacy Preserving Maximum Consensus." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402925.
Full textZhang, Sai, Cihan Tepedelenlioglu, Mahesh K. Banavar, and Andreas Spanias. "Max-consensus using the soft maximum." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810313.
Full textGeorge, Jemin. "Distributed Maximum Likelihood Using Dynamic Average Consensus." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461529.
Full textWang, Zhaowei, Peng Zeng, Mingtuo Zhou, and Dong Li. "Cluster-Based Maximum Consensus Time Synchronization in IWSNs." In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring). IEEE, 2016. http://dx.doi.org/10.1109/vtcspring.2016.7504162.
Full textWang, Gang, Kai Liu, Jinxin Wang, and Rui Xue. "Distributed Maximum Correntropy Kalman Filter with Consensus Strategies." In 2021 7th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2021. http://dx.doi.org/10.1109/iccar52225.2021.9463508.
Full textLe, Huu, Tat-Jun Chin, and David Suter. "RATSAC - Random Tree Sampling for Maximum Consensus Estimation." In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2017. http://dx.doi.org/10.1109/dicta.2017.8227480.
Full textLe, Huu, Tat-Jun Chin, and David Suter. "An Exact Penalty Method for Locally Convergent Maximum Consensus." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.48.
Full textZhang, Erchuan, David Suter, Ruwan Tennakoon, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, and Syed Zulqarnain Gilani. "Maximum Consensus by Weighted Influences of Monotone Boolean Functions." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00876.
Full textHe, Jianping, Peng Cheng, Ling Shi, and Jiming Chen. "Time synchronization in WSNs: A maximum value based consensus approach." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6161443.
Full textHasan, Nazmul. "Maximum Allowable Speed on Curve." In 2011 Joint Rail Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/jrc2011-56007.
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