Conferences related to Multivariate regression

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.


2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

All topics related to engineering and technology management, including applicable analytical methods and economical/social/human issues to be considered in making engineering decisions.


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Periodicals related to Multivariate regression

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on

Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.


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Most published Xplore authors for Multivariate regression

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Xplore Articles related to Multivariate regression

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Application of regression analysis to reduction of multivariable control problems and to process identification

Sixth Symposium on Adaptive Processes, 1967

The paper is concerned with the application of multivariate regression analysis to the reduction of a many-variable control problem and to the identification of linear and nonlinear time-varying processes. Reduction is performed by grouping the input and output variables of a many-variable process into a small number of groups of variables. Control is exercised in terms of a few variables, ...


Characterization of the mechanosensitivity of tactile receptors using multivariate logistical regression

Proceedings of the IEEE 27th Annual Northeast Bioengineering Conference (Cat. No.01CH37201), 2001

The authors' initial objective was to establish a framework for modeling afferent mechanoreceptor behavior under dynamic compressive loads using multivariate regression techniques. A multivariate logistical model of the system was chosen because the system contains continuous input variables and a singular binary output variable corresponding to an "all-or-nothing" nerve action potential. Subsequently, this method was used to quantitatively assess the ...


Multivariate regression methods for crossed-field amplifiers analyzing tube performance at final test and cathode quality

Abstracts. International Vacuum Electronics Conference 2000 (Cat. No.00EX392), 2000

Cathode technology for Crossed-Field Amplifiers (CFAs) has progressed from hot oxide types such as oxide powders, cermets, and dispensers, to cold secondary emitter types, such as Platinum and Beryllium. However, some production designs still require the usage of the hot oxide cathodes. This is due to costly on-going development and lack of availability of special cold-type cathode materials needed for ...


The Application of the Least Square Method in Back Analysis of 3D Initial Geostress

2009 International Conference on Artificial Intelligence and Computational Intelligence, 2009

According to in situ measurement, the simplified geological model is established and calculated by FLAC<sup>3D</sup>. Then, a multivariate regression model is built between the measured and calculated results of geostress at measured points. Based on the measured geostress, the regression analysis is carried out by the least square method. Through multiple regression analysis, the optimal regression coefficient can be derived. ...


A robust multivariate regression algorithm with robust estimators

[Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics, 1992

The robust regression problem is considered. A (1- in )-fraction of the given data, 0 < in < 1/2, obeys a multivariate linear model with unknown parameters. The remaining in -fraction obeys a completely different model or models. The authors develop a procedure for estimating the parameters of the linear model from the contaminated data. They use a divide-and-conquer approach. ...


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Educational Resources on Multivariate regression

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IEEE-USA E-Books

  • Application of regression analysis to reduction of multivariable control problems and to process identification

    The paper is concerned with the application of multivariate regression analysis to the reduction of a many-variable control problem and to the identification of linear and nonlinear time-varying processes. Reduction is performed by grouping the input and output variables of a many-variable process into a small number of groups of variables. Control is exercised in terms of a few variables, each representing such a group. Regression is further applied to the identification of linear and nonlinear multivariable processes where no apriori information of the dynamic characteristics is available. The resulting identification subroutines are conveniently incorporated in control procedures based on predictive-adaptive control and on dynamic programming.

  • Characterization of the mechanosensitivity of tactile receptors using multivariate logistical regression

    The authors' initial objective was to establish a framework for modeling afferent mechanoreceptor behavior under dynamic compressive loads using multivariate regression techniques. A multivariate logistical model of the system was chosen because the system contains continuous input variables and a singular binary output variable corresponding to an "all-or-nothing" nerve action potential. Subsequently, this method was used to quantitatively assess the sensitivity of rapidly adapting afferents in rat hairy skin to the stimulus metrics stress, strain, and their time derivatives. In-vitro experiments involving compressive stimulation of isolated afferents using pseudorandom and non-repeating noise sequences were completed and an analysis of the data was performed using multivariate logistical regression.

  • Multivariate regression methods for crossed-field amplifiers analyzing tube performance at final test and cathode quality

    Cathode technology for Crossed-Field Amplifiers (CFAs) has progressed from hot oxide types such as oxide powders, cermets, and dispensers, to cold secondary emitter types, such as Platinum and Beryllium. However, some production designs still require the usage of the hot oxide cathodes. This is due to costly on-going development and lack of availability of special cold-type cathode materials needed for these certain product lines. Unfortunately, several oxide powders that have been used for years are no longer produced, forcing engineers to explore other oxide powder combinations. Also, the nature of the hot oxide cathode manufacturing process allows for a margin of cathode variability, ultimately leading to finished device test variability and short cathode life in the field.

  • The Application of the Least Square Method in Back Analysis of 3D Initial Geostress

    According to in situ measurement, the simplified geological model is established and calculated by FLAC<sup>3D</sup>. Then, a multivariate regression model is built between the measured and calculated results of geostress at measured points. Based on the measured geostress, the regression analysis is carried out by the least square method. Through multiple regression analysis, the optimal regression coefficient can be derived. By comparing the influence on initial geostress field of taking account of fault and taking no account of fault, the distribution tendency of initial geostress field can be obtained. The geostress regression analysis results indicate that the calculated results are in a good agreement with the in situ measured data and can be used to engineering practice.

  • A robust multivariate regression algorithm with robust estimators

    The robust regression problem is considered. A (1- in )-fraction of the given data, 0 < in < 1/2, obeys a multivariate linear model with unknown parameters. The remaining in -fraction obeys a completely different model or models. The authors develop a procedure for estimating the parameters of the linear model from the contaminated data. They use a divide-and-conquer approach. They solve the problem using only one-dimensional robust estimators like that of P. J. Huber (1981). They illustrate their method with two examples which pose considerable difficulty to all methods except the LMS (least mean squares) method. The computational load of the present method is a small fraction of that of the LMS method.<<ETX>>

  • Multivariate regression model selection with KIC for extrapolation cases

    The Kullback information criterion, KIC and its multivariate bias-corrected version, KIC/sub VC/ are two alternatively developed criteria for model selection. The two criteria can be viewed as estimators of the expected Kullback symmetric divergence. In this paper, a new criterion is proposed in order to select a well fitted model for an extrapolation case. The proposed criterion is named, PKIC, where "P" stands for prediction, and is derived as an exact unbiased estimator of an adapted cost function that is based on the Kullback symmetric divergence and the future design matrix. PKIC is an unbiased estimator of its cost function assuming that the true model is correctly specified or overfitted. A simulation study illustrating that model selection with PKIC performs well for some extrapolation cases is presented.

  • Notice of Retraction&lt;br&gt;Market valuation and acquisition quality

    Using a sample of 700 acquisition announced in the Shanghai security market during 2002 and 2006, with the help of event study and multivariate regression framework, this article investigates whether acquisitions occurring during booming markets are fundamentally different from those occurring during depressed markets. The results shows that in the short-run, the market seems to look more favorably upon acquisition announcements during high-valuation markets, and in the long-run, acquirers buying during low-valuation markets create significantly more shareholder wealth.

  • Empirical Analysis on Effects of Share Retention and Lockup on IPO Underpricing

    In this paper, with the data of the 226 companies listed on Shanghai security exchange and Shenzhen security exchange during sample period between January 2006 and September 2008, the effects of company owner's retention and lockup on underpricing are investigated by using the multivariate regression model. The regression results show that the share retention and lockup ratio is positively correlated to IPO underpricing in Shanghai stock market, but in Shenzhen stock market these two factors negatively affect on IPO underpricing; The results of the regression of lockup period show that, whether in Shanghai or Shenzhen stock market, the lockup period is not related to the underpricing significantly.

  • Streptomycin fermentation process modeling with principal component analysis and fuzzy model

    Analysis, modeling and control for a fed-batch fermentation process still remain challenging issues. Based on principal component analysis (PCA) and fuzzy modelling a simple and efficient approach to monitoring fed-batch streptomycin fermentation is presented. The data obtained from an industrial streptomycin fermentation process is first analyzed with PCA so that the large multivariate data with highly correlated and noisy measurements can be compressed into a lower dimension space which contains most of the variance of the original matrix. Moreover, a fuzzy model is used to construct a product (antibiotic) concentration estimator of the streptomycin fermentation process, prior knowledge and expertise are important in fed-batch fermentation processes. The results of the fuzzy model compared with a linear multivariate regression model indicate the potential of the fuzzy model as a state estimator of all such industrial fed-batch processes.

  • Algebraic curve fitting for multidimensional data with exact squares distance

    This paper presents a new method for fitting algebraic curves to multidimensional data using the exact squares distance between data points and the curve. Fitting smooth curves is one of the most important themes in pattern recognition and data analysis. Simple regression analysis or multivariate regression analysis are in use for a data set consisting of observations on some variables which can be treated one of them as response variable and the others as explanatory variables. However, these analyses do not work well for a data set whose variables can not be distinguished between response and explanatory. We must prepare two algorithms to realize a method for fitting algebraic curves to data. The first is an algorithm for evaluating "distance" between data points and a given curve. The second is to find a fitting algebraic curve based on the "distance". Taubin (1991) proposed an algorithm to find the algebraic curve such that the sum of the approximate squares distance between data points and the curve is minimum. The approximate squares distance does not always agree with exact squares distance. We develop an algorithm for evaluating the exact distance between them. The algorithm is based on the Newton-Rapson method, and the amount of computation is reasonable. We show the differences between the exact distance and the approximate distance with a numerical example. The partial derivatives of the sum of the exact squares distance are also shown for the algorithm to find the fitting curve based on the exact distances.



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