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Artykuły w czasopismach na temat "Online algorithms with recourse"
Vasilopoulos, Vasileios, Georgios Pavlakos, Karl Schmeckpeper, Kostas Daniilidis i Daniel E. Koditschek. "Reactive navigation in partially familiar planar environments using semantic perceptual feedback". International Journal of Robotics Research 41, nr 1 (22.10.2021): 85–126. http://dx.doi.org/10.1177/02783649211048931.
Pełny tekst źródłaAbdelkader, Krifa, i Bouzrara Kais. "Robust H∞ gain neuro-adaptive observer design for nonlinear uncertain systems". Transactions of the Institute of Measurement and Control 41, nr 8 (17.09.2018): 2293–309. http://dx.doi.org/10.1177/0142331218798685.
Pełny tekst źródłaAngelopoulos, Spyros, Christoph Dürr i Shendan Jin. "Online maximum matching with recourse". Journal of Combinatorial Optimization 40, nr 4 (3.09.2020): 974–1007. http://dx.doi.org/10.1007/s10878-020-00641-w.
Pełny tekst źródłaAvitabile, T., C. Mathieu i L. Parkinson. "Online constrained optimization with recourse". Information Processing Letters 113, nr 3 (luty 2013): 81–86. http://dx.doi.org/10.1016/j.ipl.2012.09.011.
Pełny tekst źródłaWang, Jinde. "Approximate nonlinear programming algorithms for solving stochastic programs with recourse". Annals of Operations Research 31, nr 1 (grudzień 1991): 371–84. http://dx.doi.org/10.1007/bf02204858.
Pełny tekst źródłaKulkarni, Ankur A., i Uday V. Shanbhag. "Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms". Computational Optimization and Applications 51, nr 1 (12.02.2010): 77–123. http://dx.doi.org/10.1007/s10589-010-9316-8.
Pełny tekst źródłaMegow, Nicole, Martin Skutella, José Verschae i Andreas Wiese. "The Power of Recourse for Online MST and TSP". SIAM Journal on Computing 45, nr 3 (styczeń 2016): 859–80. http://dx.doi.org/10.1137/130917703.
Pełny tekst źródłaSmale, Steve, i Yuan Yao. "Online Learning Algorithms". Foundations of Computational Mathematics 6, nr 2 (23.09.2005): 145–70. http://dx.doi.org/10.1007/s10208-004-0160-z.
Pełny tekst źródłaBARBAKH, WESAM, i COLIN FYFE. "ONLINE CLUSTERING ALGORITHMS". International Journal of Neural Systems 18, nr 03 (czerwiec 2008): 185–94. http://dx.doi.org/10.1142/s0129065708001518.
Pełny tekst źródłaWang, Paul Y., Sainyam Galhotra, Romila Pradhan i Babak Salimi. "Demonstration of generating explanations for black-box algorithms using Lewis". Proceedings of the VLDB Endowment 14, nr 12 (lipiec 2021): 2787–90. http://dx.doi.org/10.14778/3476311.3476345.
Pełny tekst źródłaRozprawy doktorskie na temat "Online algorithms with recourse"
Lowe, Wing Wah. "An exploration of stochastic decomposition algorithms for stochastic linear programs with recourse". Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186667.
Pełny tekst źródłaLi, Le. "Online stochastic algorithms". Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0031.
Pełny tekst źródłaThis thesis works mainly on three subjects. The first one is online clustering in which we introduce a new and adaptive stochastic algorithm to cluster online dataset. It relies on a quasi-Bayesian approach, with a dynamic (i.e., time-dependent) estimation of the (unknown and changing) number of clusters. We prove that this algorithm has a regret bound of the order of and is asymptotically minimax under the constraint on the number of clusters. A RJMCMC-flavored implementation is also proposed. The second subject is related to the sequential learning of principal curves which seeks to represent a sequence of data by a continuous polygonal curve. To this aim, we introduce a procedure based on the MAP of Gibbs-posterior that can give polygonal lines whose number of segments can be chosen automatically. We also show that our procedure is supported by regret bounds with sublinear remainder terms. In addition, a greedy local search implementation that incorporates both sleeping experts and multi-armed bandit ingredients is presented. The third one concerns about the work which aims to fulfilling practical tasks within iAdvize, the company which supports this thesis. It includes sentiment analysis for textual messages by using methods in both text mining and statistics, and implementation of chatbot based on nature language processing and neural networks
Shi, Tian. "Novel Algorithms for Understanding Online Reviews". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104998.
Pełny tekst źródłaDoctor of Philosophy
Nowadays, online reviews are playing an important role in our daily lives. They are also critical to the success of many e-commerce and local businesses because they can help people build trust in brands and businesses, provide insights into products and services, and improve consumers' confidence. As a large number of reviews accumulate every day, a central research problem is to build an artificial intelligence system that can understand and interact with these reviews, and further use them to offer customers better support and services. In order to tackle challenges in these applications, we first have to get an in-depth understanding of online reviews. In this dissertation, we focus on the review understanding problem and develop machine learning and natural language processing tools to understand reviews and learn structured knowledge from unstructured reviews. We have addressed the review understanding problem in three directions, including understanding a collection of reviews, understanding a single review, and understanding a piece of a review segment. In the first direction, we proposed a short-text topic modeling method to extract topics from review corpora that consist of primary complaints of consumers. In the second direction, we focused on building sentiment analysis models to predict the opinions of consumers from their reviews. Our deep learning models can provide good prediction accuracy as well as a human-understandable explanation for the prediction. In the third direction, we develop an aspect detection method to automatically extract sentences that mention certain features consumers are interested in, from reviews, which can help customers efficiently navigate through reviews and help businesses identify the advantages and disadvantages of their products.
Trippen, Gerhard Wolfgang. "Online exploration and search in graphs /". View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20TRIPPE.
Pełny tekst źródłaLi, Rongbin, i 李榕滨. "New competitive algorithms for online job scheduling". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/197555.
Pełny tekst źródłapublished_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
ALBUQUERQUE, LUIZ FERNANDO FERNANDES DE. "ONLINE ALGORITHMS ANALYSIS FOR SPONSORED LINKS SELECTION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16088@1.
Pełny tekst źródłaLinks patrocinados são aqueles que aparecem em destaque nos resultados de pesquisas em máquinas de busca na Internet e são grande fonte de receita para seus provedores. Para os anunciantes, que fazem ofertas por palavras-chave para aparecerem em destaque nas consultas dos usuários, são uma oportunidade de divulgação da marca, conquista e manutenção de clientes. Um dos desafios das máquinas de busca neste modelo de negócio é selecionar os anunciantes que serão exibidos a cada consulta de modo a maximizar sua receita em determinado período. Este é um problema tipicamente online, onde a cada consulta é tomada uma decisão sem o conhecimento prévio das próximas consultas. Após uma decisão ser tomada, esta não pode mais ser alterada. Nesta dissertação avaliamos experimentalmente algoritmos propostos na literatura para solução deste problema, comparando-os à solução ótima offline, em simulações com dados sintéticos. Supondo que o conjunto das consultas diárias obedeça a uma determinada distribuição, propomos dois algoritmos baseados em informações estocásticas que são avaliados nos mesmos cenários que os outros algoritmos.
Sponsored links are those that appear highlighted at Internet search engine results. They are responsible for a large amount of their providers’ revenue. To advertisers, that place bids for keywords in large auctions at Internet, these links are the opportunity of brand exposing and achieving more clients. To search engine companies, one of the main challenges in this business model is selecting which advertisers should be allocated to each new query to maximize their total revenue in the end of the day. This is a typical online problem, where for each query is taken a decision without previous knowledge of future queries. Once the decision is taken, it can not be modified anymore. In this work, using synthetically generated data, we do experimental evaluation of three algorithms proposed in the literature for this problem and compare their results with the optimal offline solution. Considering that daily query set obeys some well known distribution, we propose two algorithms based on stochastic information, those are evaluated in the same scenarios of the others.
Pasteris, S. U. "Efficient algorithms for online learning over graphs". Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.
Pełny tekst źródłaBonifaci, Vincenzo. "Models and algorithms for online server routing". Doctoral thesis, La Sapienza, 2007. http://hdl.handle.net/11573/917056.
Pełny tekst źródłaHarrington, Edward Francis. "Aspects of online learning /". View thesis entry in Australian Digital Theses Program, 2004. http://thesis.anu.edu.au/public/adt-ANU20060328.160810/index.html.
Pełny tekst źródłaKamphans, Thomas. "Models and algorithms for online exploration and search". [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980408121.
Pełny tekst źródłaKsiążki na temat "Online algorithms with recourse"
Fiat, Amos, i Gerhard J. Woeginger, red. Online Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029561.
Pełny tekst źródłaKaklamanis, Christos, i Asaf Levin, red. Approximation and Online Algorithms. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2.
Pełny tekst źródłaKoenemann, Jochen, i Britta Peis, red. Approximation and Online Algorithms. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92702-8.
Pełny tekst źródłaChalermsook, Parinya, i Bundit Laekhanukit, red. Approximation and Online Algorithms. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18367-6.
Pełny tekst źródłaBampis, Evripidis, i Ola Svensson, red. Approximation and Online Algorithms. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18263-6.
Pełny tekst źródłaSanità, Laura, i Martin Skutella, red. Approximation and Online Algorithms. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28684-6.
Pełny tekst źródłaErlebach, Thomas, i Giuseppe Persiano, red. Approximation and Online Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38016-7.
Pełny tekst źródłaSolis-Oba, Roberto, i Giuseppe Persiano, red. Approximation and Online Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29116-6.
Pełny tekst źródłaJansen, Klaus, i Monaldo Mastrolilli, red. Approximation and Online Algorithms. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51741-4.
Pełny tekst źródłaSolis-Oba, Roberto, i Rudolf Fleischer, red. Approximation and Online Algorithms. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89441-6.
Pełny tekst źródłaCzęści książek na temat "Online algorithms with recourse"
Liu, Alison Hsiang-Hsuan, i Jonathan Toole-Charignon. "The Power of Amortized Recourse for Online Graph Problems". W Approximation and Online Algorithms, 134–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18367-6_7.
Pełny tekst źródłaGupta, Anupam, Vijaykrishna Gurunathan, Ravishankar Krishnaswamy, Amit Kumar i Sahil Singla. "Online Discrepancy with Recourse for Vectors and Graphs". W Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 1356–83. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2022. http://dx.doi.org/10.1137/1.9781611977073.57.
Pełny tekst źródłaFiat, Amos, i Gerhard J. Woeginger. "Competitive analysis of algorithms". W Online Algorithms, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029562.
Pełny tekst źródłaAlbers, Susanne, i Jeffery Westbrook. "Self-organizing data structures". W Online Algorithms, 13–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029563.
Pełny tekst źródłaIrani, Sandy. "Competitive analysis of paging". W Online Algorithms, 52–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029564.
Pełny tekst źródłaChrobak, Marek, i Lawrence L. Larmore. "Metrical task systems, the server problem and the work function algorithm". W Online Algorithms, 74–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029565.
Pełny tekst źródłaBartal, Yair. "Distributed paging". W Online Algorithms, 97–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029566.
Pełny tekst źródłaAspnes, James. "Competitive analysis of distributed algorithms". W Online Algorithms, 118–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029567.
Pełny tekst źródłaCsirik, János, i Gerhard J. Woeginger. "On-line packing and covering problems". W Online Algorithms, 147–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029568.
Pełny tekst źródłaAzar, Yossi. "On-line load balancing". W Online Algorithms, 178–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029569.
Pełny tekst źródłaStreszczenia konferencji na temat "Online algorithms with recourse"
Fonseca, João, Andrew Bell, Carlo Abrate, Francesco Bonchi i Julia Stoyanovich. "Setting the Right Expectations: Algorithmic Recourse Over Time". W EAAMO '23: Equity and Access in Algorithms, Mechanisms, and Optimization. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3617694.3623251.
Pełny tekst źródłaKrishnaswamy, Ravishankar, Shi Li i Varun Suriyanarayana. "Online Unrelated-Machine Load Balancing and Generalized Flow with Recourse". W STOC '23: 55th Annual ACM Symposium on Theory of Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3564246.3585222.
Pełny tekst źródłaAbé, M., i T. Igusa. "New Control Algorithms for Semi-Active Dynamic Vibration Absorbers". W ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0619.
Pełny tekst źródłaMeng, De, Maryam Fazel i Mehran Mesbahi. "Online algorithms for network formation". W 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798259.
Pełny tekst źródłaBern, M., D. H. Greene, A. Raghunathan i M. Sudan. "Online algorithms for locating checkpoints". W the twenty-second annual ACM symposium. New York, New York, USA: ACM Press, 1990. http://dx.doi.org/10.1145/100216.100264.
Pełny tekst źródłaMeyerson, Adam. "Online algorithms for network design". W the sixteenth annual ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1007912.1007958.
Pełny tekst źródłaKuh, Anthony, Muhammad Sharif Uddin i Phyllis Ng. "Online unsupervised kernel learning algorithms". W 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282179.
Pełny tekst źródłaRamanathan, Dinesh, i Rajesh Gupta. "System level online power management algorithms". W the conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/343647.343867.
Pełny tekst źródłaUddin, Muhammad Sharif, i Anthony Kuh. "Online Unsupervised Kernel Affine Projection Algorithms". W 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2018. http://dx.doi.org/10.23919/apsipa.2018.8659616.
Pełny tekst źródłaAndro-Vasko, James, Wolfgang Bein, Dara Nyknahad i Hiro Ito. "Evaluation of Online Power-Down Algorithms". W 2015 12th International Conference on Information Technology - New Generations (ITNG). IEEE, 2015. http://dx.doi.org/10.1109/itng.2015.82.
Pełny tekst źródłaRaporty organizacyjne na temat "Online algorithms with recourse"
Ur, Shmuel. Analysis of Online Algorithms for Organ Allocation. Fort Belvoir, VA: Defense Technical Information Center, październik 1990. http://dx.doi.org/10.21236/ada249361.
Pełny tekst źródłaLabrindis, Alexandros, i Nick Roussopoulos. A Performance Evaluation of Online Warehouse Update Algorithms. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1998. http://dx.doi.org/10.21236/ada441038.
Pełny tekst źródłaMathew, Jijo K., Christopher M. Day, Howell Li i 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.
Pełny tekst źródłaDanylchuk, Hanna B., i Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, sierpień 2023. http://dx.doi.org/10.31812/123456789/7732.
Pełny tekst źródłaArhin, Stephen, Babin Manandhar, Hamdiat Baba Adam i Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, kwiecień 2021. http://dx.doi.org/10.31979/mti.2021.1943.
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