Linear regression

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In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more variables denoted X. (Wikipedia.org)






Conferences related to Linear regression

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2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

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


2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.


2019 IEEE International Ultrasonics Symposium (IUS)

The conference covers all aspects of the technology associated with ultrasound generation and detection and their applications.


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.

  • 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.

  • 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)


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Periodicals related to Linear regression

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


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 ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


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-- ...


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Most published Xplore authors for Linear regression

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Xplore Articles related to Linear regression

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Predictor–Corrector Adaptation by Using Time Evolution System With Macroscopic Time Scale

[{u'author_order': 1, u'authorUrl': u'https://ieeexplore.ieee.org/author/37280302500', u'full_name': u'Shinji Watanabe', u'id': 37280302500}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37274193100', u'full_name': u'Atsushi Nakamura', u'id': 37274193100}] IEEE Transactions on Audio, Speech, and Language Processing, 2010

Incremental adaptation techniques for speech recognition are aimed at adjusting acoustic models to time-variant acoustic characteristics related to such factors as changes of speaker, speaking style, and noise source over time. In this paper, we propose a novel incremental adaptation framework, which models such time-variant characteristics by successively updating posterior distributions of acoustic model parameters based on a macroscopic time ...


Online speaker adaptation based on quasi-Bayes linear regression

[{u'author_order': 1, u'affiliation': u'Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan', u'authorUrl': u'https://ieeexplore.ieee.org/author/37269128300', u'full_name': u'Jen-Tzung Chien', u'id': 37269128300}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37281274200', u'full_name': u'Chih-Hsien Huang', u'id': 37281274200}] 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001

This paper presents an online/sequential linear regression adaptation framework for hidden Markov model (HMM) based speech recognition. Our attempt is to sequentially improve the speaker-independent (SI) speech recognizer to meet nonstationary environments via linear regression adaptation of SI HMMs. A quasi-Bayes linear regression (QBLR) algorithm is developed to execute online adaptation where the regression matrix is estimated using QB theory. ...


Kernel Eigenspace-Based MLLR Adaptation

[{u'author_order': 1, u'authorUrl': u'https://ieeexplore.ieee.org/author/37284538400', u'full_name': u'Brian Kan-Wing Mak', u'id': 37284538400}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37284793500', u'full_name': u'Roger Wend-Huu Hsiao', u'id': 37284793500}] IEEE Transactions on Audio, Speech, and Language Processing, 2007

In this paper, we propose an application of kernel methods for fast speaker adaptation based on kernelizing the eigenspace-based maximum-likelihood linear regression adaptation method. We call our new method "kernel eigenspace-based maximum-likelihood linear regression adaptation" (KEMLLR). In KEMLLR, speaker- dependent (SD) models are estimated from a common speaker-independent (SI) model using MLLR adaptation, and the MLLR transformation matrices are mapped ...


Discriminative linear-transform based adaptation using minimum verification error

[{u'author_order': 1, u'affiliation': u'Center for Signal and Image Processing, Georgia Institute of Technology, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37632869400', u'full_name': u'Sunghwan Shin', u'id': 37632869400}, {u'author_order': 2, u'affiliation': u'Speech Language Processing Team, Electronics and Telecommunications Research Institute, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37350572100', u'full_name': u'Ho-Young Jung', u'id': 37350572100}, {u'author_order': 3, u'affiliation': u'Center for Signal and Image Processing, Georgia Institute of Technology, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37985947900', u'full_name': u'Tae-Yoon Kim', u'id': 37985947900}, {u'author_order': 4, u'affiliation': u'Center for Signal and Image Processing, Georgia Institute of Technology, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/38109154700', u'full_name': u'Biing-Hwang Juang', u'id': 38109154700}] 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

This paper presents an investigation of the minimum verification error linear regression (MVELR) method for discriminative linear-transform based adaptation. The MVE criterion is employed to estimate a set of discriminative linear transformations which achieve the smallest empirical average loss with the given adaptation data. The MVELR directly minimizes the total detection errors, some of which are results of characteristic mismatch ...


Rapid Feature Space Speaker Adaptation for Multi-Stream HMM-Based Audio-Visual Speech Recognition

[{u'author_order': 1, u'affiliation': u'IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA, jghg@us.ibm.com', u'authorUrl': u'https://ieeexplore.ieee.org/author/37281266100', u'full_name': u'Jing Huang', u'id': 37281266100}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37283421400', u'full_name': u'E. Marcheret', u'id': 37283421400}, {u'author_order': 3, u'authorUrl': u'https://ieeexplore.ieee.org/author/37284910600', u'full_name': u'K. Visweswariah', u'id': 37284910600}] 2005 IEEE International Conference on Multimedia and Expo, 2005

Multi-stream hidden Markov models (HMMs) have recently been very successful in audio-visual speech recognition, where the audio and visual streams are fused at the final decision level. In this paper we investigate fast feature space speaker adaptation using multi-stream HMMs for audio-visual speech recognition. In particular, we focus on studying the performance of feature- space maximum likelihood linear regression (fMLLR), ...


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Educational Resources on Linear regression

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eLearning

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IEEE.tv Videos

Linear Regression: Intro to Machine Learning Workshop - IEEE Region 4 Presentation
Single Frame Super Resolution: Fuzzy Rule-Based and Gaussian Mixture Regression Approaches
IMS 2012 Microapps - RF System Design: Moving Beyond a Linear Datasheet
IMS 2011 Microapps - STAN Tool: A New Method for Linear and Nonlinear Stability Analysis of Microwave Circuits
A 20dBm Configurable Linear CMOS RF Power Amplifier for Multi-Standard Transmitters: RFIC Industry Showcase
A High-Efficiency Linear Power Amplifier for 28GHz Mobile Communications in 40nm CMOS: RFIC Interactive Forum 2017
Co-design of Power Amplifier and Dynamic Power Supplies for Radar and Communications Transmitters
Advances in Kernel Methods
Phase Retrieval with Application to Optical Imaging
Provably-Correct Robot Control with LTLMoP, OMPL and ROS
Sparse Fuzzy Modeling - Nikhil R Pal - WCCI 2016
Learning through Deterministic Assignment of Hidden Parameter
WIE ILC 2015 - Being Fearless: A Keynote with Patty Hatter of Intel
Tutorial: Model Predictive Control of Power Electronic Converters, Part Two, Tomislav Dragicevic - IECON 2018
Interaction of ferromagnetic and superconducting permanent magnets - superconducting levitation
Tutorial: Model Predictive Control of Power Electronic Converters, Part One, Jose Rodriguez - IECON 2018
Micro-Apps 2013: Design Methodology for GaAs MMIC PA
IEEE Magnetics 2014 Distinguished Lectures - JONATHAN COKER
Micro-Apps 2013: Breaking the RF Carrier Barrier - 0 to 200 in Under a Second
Control of a Fully-Actuated Airship for Satellite Emulation

IEEE-USA E-Books

  • Convertibility across Measurement Methods

    This chapter contains sections titled: * Introduction * Overview of Previous Convertibility Studies * A Convertibility Study of an Industry Dataset * FP to Cosmic Convertibility: Issues and Discussion

  • Regression Models in Risk Management

    This chapter discusses theory and application of generalized linear regression that minimizes a general error measure of regression residual subject to various constraints on regression coefficients and includes least-squares linear regression, median regression, quantile regression, mixed quantile regression, and robust regression as special cases. Application of generalized linear regression includes examples of financial index tracking, sparse signal reconstruction, therapy treatment planning, collateralized debt obligation, mutual fund return-based style classification, and mortgage pipeline hedging. The chapter introduces risk envelopes and risk identifiers, and also states the error decomposition theorem. It discusses special types of unconstrained and constrained linear regressions encountered in statistical decision problems. Constrained least-squares linear regression is used in an intensity- modulated radiation therapy (IMRT) treatment-planning problem. Robust regression aims to reduce influence of sample outliers on regression parameters, especially when regression error has heavy tails.

  • Parameter Estimation

    This chapter discusses techniques to estimate parameters and their accuracies. It studies two types of parameter estimation: maximum likelihood (ML) and linear regression (LR). Their accuracy and how they relate to graphical analysis are included. The relation of parameter estimation to graphical analysis is elaborated for the Weibull distribution, the exponential distribution, and the normal distribution. The chapter discusses the general aspects of parameter estimation and some characteristics of estimators in greater depth, with additional focus on the asymptotic behaviour of estimated parameters and their moments. The ML estimator is explained for both the uncensored case and the censored case. The LR estimator works with ranked plotting positions quite similar to graphical analysis with parametric plots. The chapter discusses the adjusted ranking method and the adjusted plotting position method for manipulating the ranked plotting positions.

  • Feature and Model Transformation

    This chapter contains sections titled:Feature Transformation TechniquesModel Transformation TechniquesAcoustic Model CombinationSummary and Further ReadingPrincipal Symbols



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