Littérature scientifique sur le sujet « Algorithmes online »
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Articles de revues sur le sujet "Algorithmes online"
Tornede, Alexander, Viktor Bengs et Eyke Hüllermeier. « Machine Learning for Online Algorithm Selection under Censored Feedback ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 9 (28 juin 2022) : 10370–80. http://dx.doi.org/10.1609/aaai.v36i9.21279.
Texte intégralLange, Tomer, Joseph (Seffi) Naor et Gala Yadgar. « Offline and Online Algorithms for SSD Management ». ACM SIGMETRICS Performance Evaluation Review 50, no 1 (20 juin 2022) : 89–90. http://dx.doi.org/10.1145/3547353.3522630.
Texte intégralXu, Chenyang, et Benjamin Moseley. « Learning-Augmented Algorithms for Online Steiner Tree ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 8 (28 juin 2022) : 8744–52. http://dx.doi.org/10.1609/aaai.v36i8.20854.
Texte intégralSmale, Steve, et Yuan Yao. « Online Learning Algorithms ». Foundations of Computational Mathematics 6, no 2 (23 septembre 2005) : 145–70. http://dx.doi.org/10.1007/s10208-004-0160-z.
Texte intégralBARBAKH, WESAM, et COLIN FYFE. « ONLINE CLUSTERING ALGORITHMS ». International Journal of Neural Systems 18, no 03 (juin 2008) : 185–94. http://dx.doi.org/10.1142/s0129065708001518.
Texte intégralSharma, Vishal, Kirsten E. Bray, Neha Kumar et Rebecca E. Grinter. « Romancing the Algorithm ». Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (7 novembre 2022) : 1–29. http://dx.doi.org/10.1145/3555651.
Texte intégralK, Kousalya, et Balasubramanie P. « Online Grid Scheduling Using Ant Algorithm ». International Journal of Engineering and Technology 1, no 1 (2009) : 21–26. http://dx.doi.org/10.7763/ijet.2009.v1.4.
Texte intégralMöhlmann, Mareike, Lior Zalmanson, Ola Henfridsson et Robert Wayne Gregory. « Algorithmic Management of Work on Online Labor Platforms : When Matching Meets Control ». MIS Quarterly 45, no 4 (14 octobre 2021) : 1999–2022. http://dx.doi.org/10.25300/misq/2021/15333.
Texte intégralLange, Tomer, Joseph (Seffi) Naor et Gala Yadgar. « Offline and Online Algorithms for SSD Management ». Communications of the ACM 66, no 7 (22 juin 2023) : 129–37. http://dx.doi.org/10.1145/3596205.
Texte intégralR, Velvizhi, et Jayapriya D. « Decoupling Online Algorithms from Symmetric Encryption in Hierarchical Databases ». Journal of Advanced Research in Dynamical and Control Systems 11, no 0009-SPECIAL ISSUE (25 septembre 2019) : 1004–9. http://dx.doi.org/10.5373/jardcs/v11/20192664.
Texte intégralThèses sur le sujet "Algorithmes online"
Liu, Ming. « Design and Evaluation of Algorithms for Online Machine Scheduling Problems ». Phd thesis, Ecole Centrale Paris, 2009. http://tel.archives-ouvertes.fr/tel-00453316.
Texte intégralJin, Shendan. « Online computation beyond standard models ». Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS152.
Texte intégralIn the standard setting of online computation, the input is not entirely available from the beginning, but is revealed incrementally, piece by piece, as a sequence of requests. Whenever a request arrives, the online algorithm has to make immediately irrevocable decisions to serve the request, without knowledge on the future requests. Usually, the standard framework to evaluate the performance of online algorithms is competitive analysis, which compares the worst-case performance of an online algorithm to an offline optimal solution. In this thesis, we will study some new ways of looking at online problems. First, we study the online computation in the recourse model, in which the irrevocability on online decisions is relaxed. In other words, the online algorithm is allowed to go back and change previously made decisions. More precisely, we show how to identify the trade-off between the number of re-optimization and the performance of online algorithms for the online maximum matching problem. Moreover, we study measures other than competitive analysis for evaluating the performance of online algorithms. We observe that sometimes, competitive analysis cannot distinguish the performance of different algorithms due to the worst-case nature of the competitive ratio. We demonstrate that a similar situation arises in the linear search problem. More precisely, we revisit the linear search problem and introduce a measure, which can be applied as a refinement of the competitive ratio. Last, we study the online computation in the advice model, in which the algorithm receives as input not only a sequence of requests, but also some advice on the request sequence. Specifically, we study a recent model with untrusted advice, in which the advice can be either trusted or untrusted. Assume that in the latter case, the advice can be generated from a malicious source. We show how to identify a Pareto optimal strategy for the online bidding problem in the untrusted advice model
Liu, Ming Chu Chengbin. « Design and Evaluation of Algorithms for Online Machine Scheduling Problems ». S. l. : S. n, 2009. http://theses.abes.fr/2009ECAP0028.
Texte intégralNouinou, Hajar. « Ordonnancement semi-online sur machine unitaire pour l’industrie du futur ». Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0028.
Texte intégralWe study the value of information in semi-online single machine scheduling problems. We propose semi-online algorithms to solve these problems and evaluate their performances. Unlike the classical offline scheduling problems where the decision maker knows all characteristics of the instance to be scheduled, in online scheduling problems the decision-making is performed without previous information or only with partial information about the instance. Our work consists in distinguishing information that can improve the decision-making in a semi-online scheduling context from information that, even if available, does not bring any improvement. We mainly deal with semi-online problems for minimising the total completion time on a single machine. We start by studying several semi-online models with partial information on processing times of jobs. Then, we consider the problem with information on jobs release dates and finally the combination of information on processing times and jobs release dates. For each studied problem where partial information is identified as useful, we propose a semi-online algorithm integrating the information into the decision-making. We then evaluate its performance using a competitive analysis or a comparative experimental study
Renault, Marc Paul. « Lower and upper bounds for online algorithms with advice ». Paris 7, 2014. http://www.theses.fr/2014PA077196.
Texte intégralOnline algorithms operate in a setting where the input is revealed piece by piece; the pieces are called requests. After receiving each request, online algorithms must take an action before the next request is revealed, i. E. Online algorithms must make irrevocable decisions based on the input revealed so far without any knowledge of the future input. The goal is to optimize some cost function over the input. Competitive analysis is the standard method used to analyse the quality of online algorithms. The competitive ratio is the worst case ratio, over all valid finite request sequences, of the online algorithm's performance against an optimal offline algorithm for the same request sequence. The competitive ratio compares the performance of an algorithm with no knowledge about the future against an algorithm with full knowledge about the future. Since the complete absence of future knowledge is often not a reasonable assumption, models, termed online algorithms with advice, which give the online algorithms access to a quantified amount of future knowledge, have been proposed. The interest in this model is in examining how the competitive ratio changes as a function of the amount of advice. In this thesis, we present upper and lower bounds in the advice model for classical online problems such as the k-server problem, the bin packing problem, the dual bin packing (multiple knapsack) problem, scheduling problem on m identical machines, the reordering buffer management problem and the list update problem
Teiller, Alexandre. « Aspects algorithmiques de l'optimisation « multistage » ». Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS471.
Texte intégralN a classical combinatorial optimization setting, given an instance of a problem one needs to find a good feasible solution. However, in many situations, the data may evolve over time and one has to solve a sequence of instances. Gupta et al. (2014) and Eisenstat et al. (2014) proposed a multistage model where given a time horizon the input is a sequence of instances (one for each time step), and the goal is to find a sequence of solutions (one for each time step) reaching a trade-off between the quality of the solutions in each time step and the stability/similarity of the solutions in consecutive time steps. In Chapter 1 of the thesis, we will present an overview of optimization problems tackling evolving data. Then, in Chapter 2, the multistage knapsack problem is addressed in the offline setting. The main contribution is a polynomial time approximation scheme (PTAS) for the problem in the offline setting. In Chapter 3, the multistage framework is studied for multistage problems in the online setting. The main contribution of this chapter was the introduction of a structure for these problems and almost tight upper and lower bounds on the best-possible competitive ratio for these models. Finally in chapter 4 is presented a direct application of the multistage framework in a musical context i.e. the target-based computed-assisted orchestration problem. Is presented a theoretical analysis of the problem, with NP-hardness and approximation results as well as some experimentations
Vallée, Sven. « Algorithmes d’optimisation pour un service de transport partagé à la demande ». Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0063.
Texte intégralThe purpose of this thesis is to propose efficient optimization algorithms for an on-demand common transportation system operated by Padam Mobility, a Parisian company. Formalised as a dynamic DARP, we propose three optimisation modules to tackle the underlying problem : an online module to answer real-time requests, a reinsertion module to re-insert rejected requests and a metaheuristic-based offline module to continuously optimize the rides. The proposed methods are directly implemented in the company system and extensively tested on real instances
Jankovic, Anja. « Towards Online Landscape-Aware Algorithm Selection in Numerical Black-Box Optimization ». Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS302.
Texte intégralBlack-box optimization algorithms (BBOAs) are conceived for settings in which exact problem formulations are non-existent, inaccessible, or too complex for an analytical solution. BBOAs are essentially the only means of finding a good solution to such problems. Due to their general applicability, BBOAs can exhibit different behaviors when optimizing different types of problems. This yields a meta-optimization problem of choosing the best suited algorithm for a particular problem, called the algorithm selection (AS) problem. By reason of inherent human bias and limited expert knowledge, the vision of automating the selection process has quickly gained traction in the community. One prominent way of doing so is via so-called landscape-aware AS, where the choice of the algorithm is based on predicting its performance by means of numerical problem instance representations called features. A key challenge that landscape-aware AS faces is the computational overhead of extracting the features, a step typically designed to precede the actual optimization. In this thesis, we propose a novel trajectory-based landscape-aware AS approach which incorporates the feature extraction step within the optimization process. We show that the features computed using the search trajectory samples lead to robust and reliable predictions of algorithm performance, and to powerful algorithm selection models built atop. We also present several preparatory analyses, including a novel perspective of combining two complementary regression strategies that outperforms any of the classical, single regression models, to amplify the quality of the final selector
Fersula, Jérémy. « Swarm Robotics : distributed Online Learning in the realm of Active Matter ». Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS494.
Texte intégralCPUs / GPUs, it becomes technically possible to develop small robots able to work in swarms of hundreds or thousands of units. When considering systems comprised of a large number of in- dependent robots in interaction, the individuality vanishes before the collective, and the global behavior of the ensemble has to emerge from local rules. Understanding the dynamics of large number of interacting units becomes a knowledge key to design controllable and efficient robotic swarms. This topic happens to be at the core of the field of active matter, in which the sys- tems of interest display collective effects emerging from physical interactions without computation. This thesis aims at using elements of active matter to design and understand robotic collectives, interacting both at the physical level and the software level through distributed learning algorithms. We start by studying experimentally the aggregation dynamics of a swarm of small vibrating robots performing phototaxis (i.e. search of light). The experiments are declined in different confi- gurations, either ad-hoc or implementing a distributed and online learning algorithm. This series of experiments act as a benchmark for the algorithm, showing its capabilities and limits in a real world situation. These experiments are further expanded by changing the outer shape of the robots, modifying the physical interactions by adding a force re-orientation response. This additional effect changes the global dynamics of the swarm, showing Morphological Computation at play. The new dynamics is understood through a physical model of self-alignment, allowing to extend the experimental work in sillico and hint for unseen global effects in swarms of re-orienting robots. Finally, we introduce a model of distributed learning through stochastic ODEs. This model is based on the exchange of internal degrees of freedom that couples to the dynamics of the particles, equivalents in the context of learning as a set of parameters and a controller. It shows similar results in simulation as the real-world experiments and opens up a way to a large-scale analysis of distributed and online learning dynamics
Alinia, Bahram. « Optimal resource allocation strategies for electric vehicles in smart grids ». Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0012/document.
Texte intégralWith the increased environmental concerns related to carbon emission, and rapid drop in battery prices (e.g., 35% drop in 2017), the market share of Electric Vehicles (EVs) is rapidly growing. The growing number of EVs along with the unprecedented advances in battery capacity and technology results in drastic increase in the total energy demand of EVs. This large charging demand makes the EV charging scheduling problem challenging. The critical challenge is the need for online solution design since in practical scenario the scheduler has no information of future arrivals of EVs in a time-coupled underlying problem. This thesis studies online EV scheduling problem and provides three main contributions. First, we demonstrate that the classical problem of online scheduling of deadlinesensitive jobs with partial values is similar to the EV scheduling problem and study the extension to EV charging scheduling by taking into account the processing rate limit of jobs as an additional constraint to the original problem. The problem lies in the category of time-coupled online scheduling problems without availability of future information. Using competitive ratio, as a well-established performance metric, two online algorithms, both of which are shown to be (2 − 1/U)-competitive are proposed, where U is the maximum scarcity level, a parameter that indicates demand-to-supply ratio. Second, we formulate a social welfare maximization problem for EV charging scheduling with charging capacity constraint. We devise charging scheduling algorithms that not only work in online scenario, but also they address the following two key challenges: (i) to provide on-arrival commitment; respecting the capacity constraint may hinder fulfilling charging requirement of deadline-constrained EVs entirely. Therefore, committing a guaranteed charging amount upon arrival of each EV is highly required; (ii) to guarantee (group)-strategy-proofness as a salient feature to promote EVs to reveal their true type and do not collude with other EVs. Third, we tackle online scheduling of EVs in an adaptive charging network (ACN) with local and global peak constraints. Two alternatives in resource-limited scenarios are to maximize the social welfare by partially charging the EVs (fractional model) or selecting a subset of EVs and fully charge them (integral model). For the fractional model, both offline and online algorithms are devised. We prove that the offline algorithm is optimal. We prove the online algorithm achieves a competitive ratio of 2. The integral model, however, is more challenging since the underlying problem is NP-hard due to 0/1 selection criteria of EVs. Hence, efficient solution design is challenging even in offline setting. We devise a low-complexity primal-dual scheduling algorithm that achieves a bounded approximation ratio. Built upon the offline approximate algorithm, we propose an online algorithm and analyze its competitive ratio in special cases
Livres sur le sujet "Algorithmes online"
Evripidis, Bampis, Jansen Klaus et Kenyon Claire, dir. Efficient approximation and online algorithms : Recent progress on classical combinatorical optimization problems and new applications. New York : Springer, 2006.
Trouver le texte intégralEvripidis, Bampis, Jansen Klaus et Kenyon Claire, dir. Efficient approximation and online algorithms : Recent progress on classical combinatorical optimization problems and new applications. New York : Springer, 2006.
Trouver le texte intégralGiuseppe, Persiano, et Solis-Oba Roberto, dir. Approximation and online algorithms : Second International Workshop, WAOA 2004, Bergen, Norway, September 14-16, 2004 : revised selected papers. Berlin : Springer, 2005.
Trouver le texte intégralFiat, Amos, et Gerhard J. Woeginger, dir. Online Algorithms. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029561.
Texte intégralKaklamanis, Christos, et Asaf Levin, dir. Approximation and Online Algorithms. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2.
Texte intégralKoenemann, Jochen, et Britta Peis, dir. Approximation and Online Algorithms. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92702-8.
Texte intégralChalermsook, Parinya, et Bundit Laekhanukit, dir. Approximation and Online Algorithms. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18367-6.
Texte intégralBampis, Evripidis, et Ola Svensson, dir. Approximation and Online Algorithms. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18263-6.
Texte intégralSanità, Laura, et Martin Skutella, dir. Approximation and Online Algorithms. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28684-6.
Texte intégralErlebach, Thomas, et Giuseppe Persiano, dir. Approximation and Online Algorithms. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38016-7.
Texte intégralChapitres de livres sur le sujet "Algorithmes online"
Fiat, Amos, et Gerhard J. Woeginger. « Competitive analysis of algorithms ». Dans Online Algorithms, 1–12. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029562.
Texte intégralAlbers, Susanne, et Jeffery Westbrook. « Self-organizing data structures ». Dans Online Algorithms, 13–51. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029563.
Texte intégralIrani, Sandy. « Competitive analysis of paging ». Dans Online Algorithms, 52–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029564.
Texte intégralChrobak, Marek, et Lawrence L. Larmore. « Metrical task systems, the server problem and the work function algorithm ». Dans Online Algorithms, 74–96. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029565.
Texte intégralBartal, Yair. « Distributed paging ». Dans Online Algorithms, 97–117. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029566.
Texte intégralAspnes, James. « Competitive analysis of distributed algorithms ». Dans Online Algorithms, 118–46. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029567.
Texte intégralCsirik, János, et Gerhard J. Woeginger. « On-line packing and covering problems ». Dans Online Algorithms, 147–77. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029568.
Texte intégralAzar, Yossi. « On-line load balancing ». Dans Online Algorithms, 178–95. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029569.
Texte intégralSgall, JiŘí. « On-line scheduling ». Dans Online Algorithms, 196–231. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029570.
Texte intégralBerman, Piotr. « On-line searching and navigation ». Dans Online Algorithms, 232–41. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029571.
Texte intégralActes de conférences sur le sujet "Algorithmes online"
Degroote, Hans. « Online Algorithm Selection ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/746.
Texte intégralSoma, Tasuku, et Yuichi Yoshida. « Online Risk-Averse Submodular Maximization ». Dans Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California : International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/411.
Texte intégralSardarmehni, Tohid, et Ali Heydari. « Approximate Solution for Optimal Control of Continuous-Time Switched Systems ». Dans ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9745.
Texte intégralBanerjee, Siddhartha, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha et Nisarg Shah. « Proportionally Fair Online Allocation of Public Goods with Predictions ». Dans Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California : International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/3.
Texte intégralHao, Shuji, Peilin Zhao, Yong Liu, Steven C. H. Hoi et Chunyan Miao. « Online Multitask Relative Similarity Learning ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/253.
Texte intégralYang, Peng, Peilin Zhao et Xin Gao. « Bandit Online Learning on Graphs via Adaptive Optimization ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.
Texte intégralLintzmayer, Carla Negri, Flávio Keidi Miyazawa et Eduardo Candido Xavier. « Online Circle and Sphere Packing∗ ». Dans III Encontro de Teoria da Computação. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/etc.2018.3158.
Texte intégralBanas, Ryan, Andrew McDonald et Tegwyn Perkins. « NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR ». Dans 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0070.
Texte intégralSviridov, Mikhail, Anton Mosin, Sergey Lebedev et Ron Thompson. « VENDOR-NEUTRAL STOCHASTIC INVERSION OF LWD DEEP AZIMUTHAL RESISTIVITY DATA AS A STEP TOWARD EFFICIENCY STANDARDIZATION OF GEOSTEERING SERVICES ». Dans 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0103.
Texte intégralElSayed, A., E. Kongar, S. M. Gupta et T. Sobh. « An Online Genetic Algorithm for Automated Disassembly Sequence Generation ». Dans ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48635.
Texte intégralRapports d'organisations sur le sujet "Algorithmes online"
Ur, Shmuel. Analysis of Online Algorithms for Organ Allocation. Fort Belvoir, VA : Defense Technical Information Center, octobre 1990. http://dx.doi.org/10.21236/ada249361.
Texte intégralSantesson, S., et P. Hallam-Baker. Online Certificate Status Protocol Algorithm Agility. RFC Editor, juin 2011. http://dx.doi.org/10.17487/rfc6277.
Texte intégralLabrindis, Alexandros, et Nick Roussopoulos. A Performance Evaluation of Online Warehouse Update Algorithms. Fort Belvoir, VA : Defense Technical Information Center, janvier 1998. http://dx.doi.org/10.21236/ada441038.
Texte intégralStreeter, Matthew, et Daniel Golovin. An Online Algorithm for Maximizing Submodular Functions. Fort Belvoir, VA : Defense Technical Information Center, décembre 2007. http://dx.doi.org/10.21236/ada476748.
Texte intégralBalman, Mehmet, et Tevfik Kosar. An Online Scheduling Algorithm with Advance Reservation for Large-Scale Data Transfers. Office of Scientific and Technical Information (OSTI), mai 2010. http://dx.doi.org/10.2172/1050437.
Texte intégralMathew, Jijo K., Christopher M. Day, Howell Li et Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.
Texte intégralArhin, Stephen, Babin Manandhar, Hamdiat Baba Adam et Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, avril 2021. http://dx.doi.org/10.31979/mti.2021.1943.
Texte intégralDanylchuk, Hanna B., et Serhiy O. Semerikov. Advances in machine learning for the innovation economy : in the shadow of war. Криворізький державний педагогічний університет, août 2023. http://dx.doi.org/10.31812/123456789/7732.
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