Literatura científica selecionada sobre o tema "Online smoothing"
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Artigos de revistas sobre o assunto "Online smoothing"
Chen, Sixia, e Alexander Russell. "Online Metric Tracking and Smoothing". Algorithmica 68, n.º 1 (28 de junho de 2012): 133–51. http://dx.doi.org/10.1007/s00453-012-9669-8.
Texto completo da fonteMann, B. L. "Smoothing Some Wrinkles in Online Dispute Resolution". International Journal of Law and Information Technology 17, n.º 1 (21 de novembro de 2008): 83–112. http://dx.doi.org/10.1093/ijlit/ean017.
Texto completo da fonteCao, Guohong, Wu-chi Feng e Mukesh Singhal. "Online variable-bit-rate video traffic smoothing". Computer Communications 26, n.º 7 (maio de 2003): 639–51. http://dx.doi.org/10.1016/s0140-3664(02)00197-4.
Texto completo da fonteJiang, Wei, e Yongzhong Zhu. "Online process mean estimation usingL1norm exponential smoothing". Naval Research Logistics 56, n.º 5 (agosto de 2009): 439–49. http://dx.doi.org/10.1002/nav.20351.
Texto completo da fonteCai, Qingzhong, Gongliu Yang, Ningfang Song, Jianye Pan e Yiliang Liu. "An Online Smoothing Method Based on Reverse Navigation for ZUPT-Aided INSs". Journal of Navigation 70, n.º 2 (21 de outubro de 2016): 342–58. http://dx.doi.org/10.1017/s0373463316000667.
Texto completo da fonteDuffield, Samuel, e Sumeetpal Singh. "Online Particle Smoothing With Application to Map-Matching". IEEE Transactions on Signal Processing 70 (2022): 497–508. http://dx.doi.org/10.1109/tsp.2022.3141259.
Texto completo da fonteSen, S., J. L. Rexford, J. K. Dey, J. F. Kurose e D. F. Towsley. "Online smoothing of variable-bit-rate streaming video". IEEE Transactions on Multimedia 2, n.º 1 (março de 2000): 37–48. http://dx.doi.org/10.1109/6046.825793.
Texto completo da fonteEinbeck, Jochen, e Göran Kauermann. "Online monitoring with local smoothing methods and adaptive ridging". Journal of Statistical Computation and Simulation 73, n.º 12 (dezembro de 2003): 913–29. http://dx.doi.org/10.1080/0094965031000104332.
Texto completo da fonteEkadjaja, Agustin, Andre Chuandra e Margarita Ekadjaja. "THE IMPACT OF BOARD INDEPENDENCE, PROFITABILITY, LEVERAGE, AND FIRM SIZE ON INCOME SMOOTHING IN CONTROL OF AGENCY CONFLICT". Jurnal Ekonomi Manajemen Sistem Informasi 1, n.º 3 (27 de fevereiro de 2020): 238–47. http://dx.doi.org/10.31933/jemsi.v1i3.104.
Texto completo da fonteEkadjaja, Agustin, Andre Chuandra e Margarita Ekadjaja. "THE IMPACT OF BOARD INDEPENDENCE, PROFITABILITY, LEVERAGE, AND FIRM SIZE ON INCOME SMOOTHING IN CONTROL OF AGENCY CONFLICT". Dinasti International Journal of Education Management And Social Science 1, n.º 3 (19 de fevereiro de 2020): 388–99. http://dx.doi.org/10.31933/dijemss.v1i3.169.
Texto completo da fonteTeses / dissertações sobre o assunto "Online smoothing"
Cunningham, Alexander G. "Scalable online decentralized smoothing and mapping". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51848.
Texto completo da fonteWesterborn, Johan. "On particle-based online smoothing and parameter inference in general state-space models". Doctoral thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215292.
Texto completo da fonteDenna avhandling består av fyra artiklar, presenterade i Paper A-D, som behandlar partikelbaserad online-glättning och parameter- skattning i generella dolda Markovkedjor. I papper A presenteras en ny algoritm, PaRIS, med målet att effek- tivt beräkna partikelbaserade online-skattningar av glättade väntevär- den av additiva tillståndsfunktionaler. Algoritmen har, under svaga villkor, en beräkningskomplexitet som växer endast linjärt med antalet partiklar samt högst begränsade minneskrav. Dessutom härleds ett an- tal konvergensresultat för denna algoritm, såsom en central gränsvärdes- sats. Algoritmen testas i en simuleringsstudie. I papper B studeras problemet att skatta marginalglättningsfördel- ningen i dolda Markovkedjor. Detta åstadkoms genom att exekvera PaRIS-algoritmen i marginalläge. Genom ett argument om mixning i Markovkedjor motiveras att avbryta uppdateringen efter en av ett stoppkriterium bestämd fördröjning vilket ger en adaptiv fördröjnings- glättare. I papper C studeras problemet att beräkna derivator av filterfördel- ningen. Dessa används för att beräkna gradienten av log-likelihood funktionen. Algoritmen, som innehåller en uppdateringsmekanism lik- nande den i PaRIS, förses med ett antal konvergensresultat, såsom en central gränsvärdessats med en varians som är likformigt begränsad. Den resulterande algoritmen används för att konstruera en rekursiv parameterskattningsalgoritm. Papper D fokuserar på online-estimering av modellparametrar i generella dolda Markovkedjor. Den presenterade algoritmen kan ses som en kombination av PaRIS algoritmen och en nyligen föreslagen online-implementation av den klassiska EM-algoritmen.
QC 20171009
Westerborn, Johan. "On particle-based online smoothing and parameter inference in general hidden Markov models". Licentiate thesis, KTH, Matematik (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166629.
Texto completo da fonteDenna avhandling består av två artiklar som behandlar inferens i dolda Markovkedjor med generellt tillståndsrum via sekventiella Monte Carlo-metoder. Den första artikeln presenterar en ny algoritm, PaRIS, med målet att effektivt beräkna partikelbaserade online-skattningar av utjämnade väntevärden av additiva tillståndsfunktionaler. Algoritmen har, under svaga villkor, en beräkningkomplexitet som växer endast linjärt med antalet partiklar samt h\ögst begränsade minneskrav. Dessutom härleds ett antal konvergensresultat för denna algoritm, såsom en central gränsvärdessats. Den andra artikeln fokuserar på online-estimering av modellparametrar i en generella dolda Markovkedjor. Den presenterade algoritmen kan ses som en kombination av PaRIS och en nyligen föreslagen online-implementation av den klassiska EM-algoritmen.
QC 20150521
WANG, Yanan. "Exploring online brand choice at the SKU level : the effects of internet-specific attributes". Digital Commons @ Lingnan University, 2004. https://commons.ln.edu.hk/mkt_etd/13.
Texto completo da fonteMartin, Alice. "Deep learning models and algorithms for sequential data problems : applications to language modelling and uncertainty quantification". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS007.
Texto completo da fonteIn this thesis, we develop new models and algorithms to solve deep learning tasks on sequential data problems, with the perspective of tackling the pitfalls of current approaches for learning language models based on neural networks. A first research work develops a new deep generative model for sequential data based on Sequential Monte Carlo Methods, that enables to better model diversity in language modelling tasks, and better quantify uncertainty in sequential regression problems. A second research work aims to facilitate the use of SMC techniques within deep learning architectures, by developing a new online smoothing algorithm with reduced computational cost, and applicable on a wider scope of state-space models, including deep generative models. Finally, a third research work proposes the first reinforcement learning that enables to learn conditional language models from scratch (i.e without supervised datasets), based on a truncation mechanism of the natural language action space with a pretrained language model
Vestin, Albin, e Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms". Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
Texto completo da fonteSu, Aron Wei-Hsiang. "ECG Noise Filtering Using Online Model-Based Bayesian Filtering Techniques". Thesis, 2013. http://hdl.handle.net/10012/7917.
Texto completo da fonteLivros sobre o assunto "Online smoothing"
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Encontre o texto completo da fonteQuickBooks® Pro Support+1(866∎751∎2963)Phone Number. mrinalt, 2022.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Online smoothing"
Gray, Chris, Maarten Löffler e Rodrigo I. Silveira. "Smoothing Imprecise 1.5D Terrains". In Approximation and Online Algorithms, 214–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93980-1_17.
Texto completo da fonteLin, Jeng-Wei, Ray-I. Chang, Jan-Ming Ho e Feipei Lai. "Aggressive Traffic Smoothing for Delivery of Online Multimedia". In Advances in Multimedia Information Processing - PCM 2004, 114–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30541-5_15.
Texto completo da fonteZhao, Yue, e Philipp Krähenbühl. "Real-Time Online Video Detection with Temporal Smoothing Transformers". In Lecture Notes in Computer Science, 485–502. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19830-4_28.
Texto completo da fonteQuicke, Donald L. J., Buntika A. Butcher e Rachel A. Kruft Welton. "Dates and Julian dates." In Practical R for biologists: an introduction, 227–39. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0020.
Texto completo da fonteQuicke, Donald L. J., Buntika A. Butcher e Rachel A. Kruft Welton. "Dates and Julian dates." In Practical R for biologists: an introduction, 227–39. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0227.
Texto completo da fonteZhang, Xinxin, Dike Li, Zeyuan Cheng, Jianqin Zhu, Zhi Tao e Lu Qiu. "Rapid Online Estimation of Time-Varying Thermal Boundary Conditions in Convective Heat Transfer Problem by ANN-Based Extended Kalman Smoothing Algorithm". In Computational and Experimental Simulations in Engineering, 203–18. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-44947-5_17.
Texto completo da fonteDařena, František, Jonáš Petrovský, Jan Přichystal e Jan Žižka. "Using Online Data in Predicting Stock Price Movements". In Research Anthology on Strategies for Using Social Media as a Service and Tool in Business, 1056–83. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9020-1.ch053.
Texto completo da fonteDařena, František, Jonáš Petrovský, Jan Přichystal e Jan Žižka. "Using Online Data in Predicting Stock Price Movements". In Techno-Social Systems for Modern Economical and Governmental Infrastructures, 125–59. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5586-5.ch006.
Texto completo da fonteBaibarac Duignan, Corelia, Julieta Matos Castaño, Anouk Geenen e Michiel de Lange. "Controversing Datafication through Media Architectures". In Situating Data. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463722971_ch03.
Texto completo da fonteWang, Xuerui, Wei Li, Ying Cui, Ruofei Zhang e Jianchang Mao. "Click-Through Rate Estimation for Rare Events in Online Advertising". In Advances in Multimedia and Interactive Technologies, 1–12. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-189-8.ch001.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Online smoothing"
Abuolaim, Abdullah, e Michael S. Brown. "Online Lens Motion Smoothing for Video Autofocus". In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093558.
Texto completo da fonteGao, Kui, Wen Gao, Simin He e Yuan Zhang. "Online smoothing for scalable media stream delivery". In Electronic Imaging 2004, editado por Sethuraman Panchanathan e Bhaskaran Vasudev. SPIE, 2004. http://dx.doi.org/10.1117/12.526367.
Texto completo da fonteLin, Jimmy, Rion Snow e William Morgan. "Smoothing techniques for adaptive online language models". In the 17th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020476.
Texto completo da fonteZhang, Jin, Kai Liu, Fei Cheng e YunSong Li. "Online visual tracking based on updating with smoothing". In SPIE Sensing Technology + Applications, editado por Bormin Huang, Chein-I. Chang e José Fco López. SPIE, 2014. http://dx.doi.org/10.1117/12.2053153.
Texto completo da fonteKanagal, Bhargav, e Amol Deshpande. "Online Filtering, Smoothing and Probabilistic Modeling of Streaming data". In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497525.
Texto completo da fonteDang, Kang, Jiong Yang e Junsong Yuan. "Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps". In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.367.
Texto completo da fonteWesterborn, Johan, e Jimmy Olsson. "Efficient particle-based online smoothing in general hidden Markov models". In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855159.
Texto completo da fonteAbdelli, Ahmed. "Recursive motion smoothing for online video stabilization in wide-area surveillance". In 2016 International Conference on Big Data and Smart Computing (BigComp). IEEE, 2016. http://dx.doi.org/10.1109/bigcomp.2016.7425799.
Texto completo da fontePark, Yun S., Leigh R. Hochberg, Emad N. Eskandar, Sydney S. Cash e Wilson Truccolo. "Adaptive parametric spectral estimation with Kalman smoothing for online early seizure detection". In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6696207.
Texto completo da fonteYildirim, Sinan, e A. Taylan Cemgil. "Forward smoothing and online expectation-maximisation in Gaussian linear state-space models". In 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU). IEEE, 2011. http://dx.doi.org/10.1109/siu.2011.5929704.
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