Literatura académica sobre el tema "Multiple Sparse Bayesian Learning"
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Artículos de revistas sobre el tema "Multiple Sparse Bayesian Learning"
Nannuru, Santosh, Kay L. Gemba, Peter Gerstoft, William S. Hodgkiss y Christoph F. Mecklenbräuker. "Sparse Bayesian learning with multiple dictionaries". Signal Processing 159 (junio de 2019): 159–70. http://dx.doi.org/10.1016/j.sigpro.2019.02.003.
Texto completoZhang, Shuanghui, Yongxiang Liu y Xiang Li. "Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning". Sensors 17, n.º 10 (10 de octubre de 2017): 2295. http://dx.doi.org/10.3390/s17102295.
Texto completoShin, Myoungin, Wooyoung Hong, Keunhwa Lee y Youngmin Choo. "Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning". Sensors 22, n.º 21 (4 de noviembre de 2022): 8511. http://dx.doi.org/10.3390/s22218511.
Texto completoSun, Bin, Haowen Chen, Xizhang Wei y Xiang Li. "Multitarget Direct Localization Using Block Sparse Bayesian Learning in Distributed MIMO Radar". International Journal of Antennas and Propagation 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/903902.
Texto completoShin, Myoungin, Wooyoung Hong, Keunhwa Lee y Youngmin Choo. "Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning". Sensors 21, n.º 17 (30 de agosto de 2021): 5827. http://dx.doi.org/10.3390/s21175827.
Texto completoHu, Xiaowei, Ningning Tong, Xingyu He y Yuchen Wang. "2D Superresolution ISAR Imaging via Temporally Correlated Multiple Sparse Bayesian Learning". Journal of the Indian Society of Remote Sensing 46, n.º 3 (12 de octubre de 2017): 387–93. http://dx.doi.org/10.1007/s12524-017-0709-3.
Texto completoYuan, Cheng y Mingjun Su. "Seismic spectral sparse reflectivity inversion based on SBL-EM: experimental analysis and application". Journal of Geophysics and Engineering 16, n.º 6 (18 de octubre de 2019): 1124–38. http://dx.doi.org/10.1093/jge/gxz082.
Texto completoNarayanaswamy, Anughna y Ramesha Muniyappa. "Underdetermined direction of arrival estimation for multiple input and multiple outputs sparse channel based on Bayesian learning framework". Indonesian Journal of Electrical Engineering and Computer Science 31, n.º 1 (1 de julio de 2023): 170. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp170-179.
Texto completoQin, Yanhua, Yumin Liu y Zhongyuan Yu. "Underdetermined DOA estimation using coprime array via multiple measurement sparse Bayesian learning". Signal, Image and Video Processing 13, n.º 7 (22 de abril de 2019): 1311–18. http://dx.doi.org/10.1007/s11760-019-01480-x.
Texto completoMa, Jitong, Jiacheng Zhang, Zhengyan Yang y Tianshuang Qiu. "Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise". Sensors 22, n.º 16 (20 de agosto de 2022): 6268. http://dx.doi.org/10.3390/s22166268.
Texto completoTesis sobre el tema "Multiple Sparse Bayesian Learning"
Higson, Edward John. "Bayesian methods and machine learning in astrophysics". Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.
Texto completoParisi, Simone [Verfasser], Jan [Akademischer Betreuer] Peters y Joschka [Akademischer Betreuer] Boedeker. "Reinforcement Learning with Sparse and Multiple Rewards / Simone Parisi ; Jan Peters, Joschka Boedeker". Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1203301545/34.
Texto completoTandon, Prateek. "Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations". Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/658.
Texto completoTiclavilca, Andres M. "Multivariate Bayesian Machine Learning Regression for Operation and Management of Multiple Reservoir, Irrigation Canal, and River Systems". DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/600.
Texto completoJin, Junyang. "Novel methods for biological network inference : an application to circadian Ca2+ signaling network". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/285323.
Texto completoYazdani, Akram. "Statistical Approaches in Genome-Wide Association Studies". Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423743.
Texto completoLo Studio di Associazione Genome-Wide, GWAS, tipicamente comprende centinaia di migliaia di polimorfismi a singolo nucleotide, SNPs, genotipizzati per pochi campioni. L'obiettivo di tale studio consiste nell'individuare le regioni cruciali SNPs e prevedere gli esiti di una variabile risposta. Dal momento che il numero di predittori è di gran lunga superiore al numero di campioni, non è possibile condurre l'analisi dei dati con metodi statistici classici. GWAS attuali, i metodi negli maggiormente utilizzati si basano sull'analisi a marcatore unico, che valuta indipendentemente l'associazione di ogni SNP con i tratti complessi. A causa della bassa potenza dell'analisi a marcatore unico nel rilevamento delle associazioni reali, l'analisi simultanea ha recentemente ottenuto più attenzione. I recenti metodi per l'analisi simultanea nel multidimensionale hanno una limitazione sulla disparità tra il numero di predittori e il numero di campioni. Pertanto, è necessario ridurre la dimensionalità dell'insieme di SNPs. Questa tesi fornisce una panoramica dell'analisi a marcatore singolo e dell'analisi simultanea, focalizzandosi su metodi Bayesiani. Vengono discussi i limiti di tali approcci in relazione ai GWAS, con riferimento alla letteratura recente e utilizzando studi di simulazione. Per superare tali problemi, si è cercato di ridurre la dimensione dell'insieme di SNPs con una tecnica a proiezione casuale. Poiché questo approccio non comporta miglioramenti nella accuratezza predittiva del modello, viene quindi proposto un approccio in due fasi, che risulta essere un metodo ibrido di analisi singola e simultanea. Tale approccio, completamente Bayesiano, seleziona gli SNPs più promettenti nella prima fase valutando l'impatto di ogni marcatore indipendentemente. Nella seconda fase, viene sviluppato un modello gerarchico Bayesiano per analizzare contemporaneamente l'impatto degli indicatori selezionati. Il modello che considera i campioni correlati pone una priori locale-globale ristretta sugli effetti dei marcatori. Tale prior riduce a zero gli effetti piccoli, mentre mantiene gli effetti più grandi relativamente grandi. Le priori specificate sugli effetti dei marcatori sono rappresentazioni gerarchiche della distribuzione Pareto doppia; queste a priori migliorano le prestazioni predittive del modello. Infine, nella tesi vengono riportati i risultati dell'analisi su dati reali di SNP basate sullo studio a marcatore singolo e sul nuovo approccio a due stadi.
Deshpande, Hrishikesh. "Dictionary learning for pattern classification in medical imaging". Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S032/document.
Texto completoMost natural signals can be approximated by a linear combination of a few atoms in a dictionary. Such sparse representations of signals and dictionary learning (DL) methods have received a special attention over the past few years. While standard DL approaches are effective in applications such as image denoising or compression, several discriminative DL methods have been proposed to achieve better image classification. In this thesis, we have shown that the dictionary size for each class is an important factor in the pattern recognition applications where there exist variability difference between classes, in the case of both the standard and discriminative DL methods. We validated the proposition of using different dictionary size based on complexity of the class data in a computer vision application such as lips detection in face images, followed by more complex medical imaging application such as classification of multiple sclerosis (MS) lesions using MR images. The class specific dictionaries are learned for the lesions and individual healthy brain tissues, and the size of the dictionary for each class is adapted according to the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients
Chen, Cong. "High-Dimensional Generative Models for 3D Perception". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.
Texto completoDoctor of Philosophy
The development of automation systems and robotics brought the modern world unrivaled affluence and convenience. However, the current automated tasks are mainly simple repetitive motions. Tasks that require more artificial capability with advanced visual cognition are still an unsolved problem for automation. Many of the high-level cognition-based tasks require the accurate visual perception of the environment and dynamic objects from the data received from the optical sensor. The capability to represent, identify and interpret complex visual data for understanding the geometric structure of the world is 3D perception. To better tackle the existing 3D perception challenges, this dissertation proposed a set of generative learning-based frameworks on sparse tensor data for various high-dimensional robotics perception applications: underwater point cloud filtering, image restoration, deformation detection, and localization. Underwater point cloud data is relevant for many applications such as environmental monitoring or geological exploration. The data collected with sonar sensors are however subjected to different types of noise, including holes, noise measurements, and outliers. In the first chapter, we propose a generative model for point cloud data recovery using Variational Bayesian (VB) based sparse tensor factorization methods to tackle these three defects simultaneously. In the second part of the dissertation, we propose an image restoration technique to tackle missing data, which is essential for many perception applications. An efficient generative chaotic RNN framework has been introduced for recovering the sparse tensor from a single corrupted image for various types of missing data. In the last chapter, a multi-level CNN for high-dimension tensor feature extraction for underwater vehicle localization has been proposed.
Subramanian, Harshavardhan. "Combining scientific computing and machine learning techniques to model longitudinal outcomes in clinical trials". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176427.
Texto completoFrancisco, André Biasin Segalla. "Esparsidade estruturada em reconstrução de fontes de EEG". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-13052018-112615/.
Texto completoFunctional Neuroimaging is an area of neuroscience which aims at developing several techniques to map the activity of the nervous system and has been under constant development in the last decades due to its high importance in clinical applications and research. Common applied techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have great spatial resolution (~ mm), but a limited temporal resolution (~ s), which poses a great challenge on our understanding of the dynamics of higher cognitive functions, whose oscillations can occur in much finer temporal scales (~ ms). Such limitation occurs because these techniques rely on measurements of slow biological responses which are correlated in a complicated manner to the actual electric activity. The two major candidates that overcome this shortcoming are Electro- and Magnetoencephalography (EEG/MEG), which are non-invasive techniques that measure the electric and magnetic fields on the scalp, respectively, generated by the electrical brain sources. Both have millisecond temporal resolution, but typically low spatial resolution (~ cm) due to the highly ill-posed nature of the electromagnetic inverse problem. There has been a huge effort in the last decades to improve their spatial resolution by means of incorporating relevant information to the problem from either other imaging modalities and/or biologically inspired constraints allied with the development of sophisticated mathematical methods and algorithms. In this work we focus on EEG, although all techniques here presented can be equally applied to MEG because of their identical mathematical form. In particular, we explore sparsity as a useful mathematical constraint in a Bayesian framework called Sparse Bayesian Learning (SBL), which enables the achievement of meaningful unique solutions in the source reconstruction problem. Moreover, we investigate how to incorporate different structures as degrees of freedom into this framework, which is an application of structured sparsity and show that it is a promising way to improve the source reconstruction accuracy of electromagnetic imaging methods.
Libros sobre el tema "Multiple Sparse Bayesian Learning"
Donovan, Therese y Ruth M. Mickey. Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.001.0001.
Texto completoGottlieb, Jacqueline. Neuronal Mechanisms of Attentional Control. Editado por Anna C. (Kia) Nobre y Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.033.
Texto completoCapítulos de libros sobre el tema "Multiple Sparse Bayesian Learning"
Chatzis, Sotirios P. "Sparse Bayesian Recurrent Neural Networks". En Machine Learning and Knowledge Discovery in Databases, 359–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_22.
Texto completoNaik, Cian, François Caron, Judith Rousseau, Yee Whye Teh y Konstantina Palla. "Bayesian Nonparametrics for Sparse Dynamic Networks". En Machine Learning and Knowledge Discovery in Databases, 191–206. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26419-1_12.
Texto completoHuang, Yong y James L. Beck. "Sparse Bayesian Learning and its Application in Bayesian System Identification". En Bayesian Inverse Problems, 79–111. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/b22018-7.
Texto completoZhang, Guanghao, Dongshun Cui, Shangbo Mao y Guang-Bin Huang. "Sparse Bayesian Learning for Extreme Learning Machine Auto-encoder". En Proceedings in Adaptation, Learning and Optimization, 319–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_34.
Texto completoLei, Yun, Xiaoqing Ding y Shengjin Wang. "Adaptive Sparse Vector Tracking Via Online Bayesian Learning". En Advances in Machine Vision, Image Processing, and Pattern Analysis, 35–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_4.
Texto completoMichel, Vincent, Evelyn Eger, Christine Keribin y Bertrand Thirion. "Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis". En Machine Learning in Medical Imaging, 50–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15948-0_7.
Texto completoSamek, Wojciech, Alexander Binder y Motoaki Kawanabe. "Multi-task Learning via Non-sparse Multiple Kernel Learning". En Computer Analysis of Images and Patterns, 335–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_41.
Texto completoWang, Lu, Lifan Zhao, Guoan Bi y Xin Liu. "Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery". En Wireless and Satellite Systems, 3–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19153-5_1.
Texto completoDu, Changying, Changde Du, Guoping Long, Xin Jin y Yucheng Li. "Efficient Bayesian Maximum Margin Multiple Kernel Learning". En Machine Learning and Knowledge Discovery in Databases, 165–81. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46128-1_11.
Texto completoSabuncu, Mert R. "A Sparse Bayesian Learning Algorithm for Longitudinal Image Data". En Lecture Notes in Computer Science, 411–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24574-4_49.
Texto completoActas de conferencias sobre el tema "Multiple Sparse Bayesian Learning"
Nannuru, Santosh, Kay L. Gemba y Peter Gerstoft. "Sparse Bayesian learning with multiple dictionaries". En 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2017. http://dx.doi.org/10.1109/globalsip.2017.8309149.
Texto completoGerstoft, Peter y Christoph F. Mecklenbrauker. "Wideband Sparse Bayesian Learning for DOA estimation from multiple snapshots". En 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2016. http://dx.doi.org/10.1109/sam.2016.7569745.
Texto completoYou, Kangyong, Wenbin Guo, Peiliang Zuo, Yueliang Liu y Wenbo Wang. "Sparse Bayesian Learning for Multiple Sources Localization with Unknown Propagation Parameters". En 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2019. http://dx.doi.org/10.1109/pimrc.2019.8904415.
Texto completoLi, Yuling, Xin Liu y Ying Liu. "Improved super-resolution optical fluctuation imaging by multiple sparse Bayesian learning method". En Optics in Health Care and Biomedical Optics VIII, editado por Qingming Luo, Xingde Li, Yuguo Tang y Ying Gu. SPIE, 2018. http://dx.doi.org/10.1117/12.2500867.
Texto completoLi, Shaoyang, Xiaoming Tao, Yang Li y Jianhua Lu. "Large-scale structured sparse image reconstruction with correlated multiple-measurement vectors using Bayesian learning". En 2015 Picture Coding Symposium (PCS). IEEE, 2015. http://dx.doi.org/10.1109/pcs.2015.7170089.
Texto completoLiu, Chang, Yicong Wang, Jin Wang, Jie Wang, Li Tian y Xiao Yu. "IoT-based Electrical Device Positioning Method Using Multiple Signal Classification and Sparse Bayesian Learning". En 2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2022. http://dx.doi.org/10.1109/icpics55264.2022.9873800.
Texto completoWu, Jie, Yibo Hu, Biyue Fan, Wei Chen y Deyan Sun. "Using nonlinear sparse Bayesian learning model to identify the correlation between multiple clinical cognitive scores and neuroimaging measurements". En 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313366.
Texto completoHe, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He y Guoping Long. "Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/254.
Texto completoSharpe, Conner, Clinton Morris, Benjamin Goldsberry, Carolyn Conner Seepersad y Michael R. Haberman. "Bayesian Network Structure Optimization for Improved Design Space Mapping for Design Exploration With Materials Design Applications". En ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67643.
Texto completoQiao, Xuechun y Yasen Wang. "Recursive Sparse Bayesian Learning". En 2022 China Automation Congress (CAC). IEEE, 2022. http://dx.doi.org/10.1109/cac57257.2022.10055431.
Texto completoInformes sobre el tema "Multiple Sparse Bayesian Learning"
Wang, Fulton y Ali Pinar. Developing an Active Learning algorithm for learning Bayesian classifiers under the Multiple Instance Learning scenario. Office of Scientific and Technical Information (OSTI), octubre de 2020. http://dx.doi.org/10.2172/1821545.
Texto completoEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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