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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2012 IEEE 12th International Conference on Data Mining (ICDM)

ICDM has established itself as the world's premier research conference in data mining covering all aspects of data mining in a wide range related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing.

  • 2011 IEEE 11th International Conference on Data Mining (ICDM)

    The conference provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. It covers all aspects of data mining and draws researchers and application developers from a wide range of data mining related areas.

  • 2010 IEEE 10th International Conference on Data Mining (ICDM)

    The IEEE International Conference on Data Mining (ICDM) has established itself as the world's premier research conference in data mining. The 10th edition of ICDM (ICDM '10) provides a leading forum for presentation of original research results, as well as exchange and dissemination of innovative,practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications

  • 2009 IEEE International Conference on Data Mining (ICDM)

    The conference covers all aspects of data mining, including algorithms, software and systems, and applications. In addition, ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing.

  • 2008 IEEE International Conference on Data Mining (ICDM)

    Conference covers all aspects of data mining,algorithms,software & systems, and applications.ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing.


2012 IEEE 15th International Conference on Computational Science and Engineering (CSE)

The Computational Science and Engineering area has earned prominence through advances in electronic and integrated technologies beginning in the 1940s. Current times are very exciting and the years to come will witness a proliferation in the use of various advanced computing systems. It is increasingly becoming an emerging and promising discipline in shaping future research and development activities in academia and industry, ranging from engineering, science, finance, economics, arts and humanitarian fields, especially when the solution of large and complex problems must cope with tight timing schedules.


2011 3rd International Conference on Networking and Digital Society (ICNDS)

The aim of ICNDS 2011 is to provide a platform for researchers and engineers to present their research results and development activities in Networking and Digital Society. It also provides opportunities for the delegates to exchange new ideas and application experiences, to find global partners for future collaboration.


2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO)

The CSO2011 conference is to bring together computational scientists, applied mathematicians, computational engineers, industrial practitioners and researchers to present, discuss and exchange ideas, results and experiences in the area of computational sciences, applied computing, optimization theory and inter-disciplinary applications.

  • 2010 Third International Joint Conference on Computational Sciences and Optimization (CSO)

    The CSO 2010 joint conference will provide an idea-exchange and discussion platform for the world s researchers and academia, where internationally recognized researchers and practitioners share cutting-edge information, address the hottest issue in computational science and optimization, explore new computational technologies, exchange and build upon new ideas. And meantime the joint conference will also provide researchers and practitioners an opportunity to highlight innovative research directions, and



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; ...


Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


Image Processing, IEEE Transactions on

Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.


Mechatronics, IEEE/ASME Transactions on

Synergetic integration of mechanical engineering with electronic and intelligent computer control in the design and manufacture of industrial products and processes. (4) (IEEE Guide for Authors) A primary purpose is to have an aarchival publication which will encompass both theory and practice. Papers will be published which disclose significant new knowledge needed to implement intelligent mechatronics systems, from analysis and ...




Xplore Articles related to Linear regression

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Dialect/Accent Classification Using Unrestricted Audio

Rongqing Huang; John H. L. Hansen; Pongtep Angkititrakul IEEE Transactions on Audio, Speech, and Language Processing, 2007

This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the ...


Agriculture yield prediction using predictive analytic techniques

S. Nagini; T. V. Rajini Kanth; B. V. Kiranmayee 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 2016

India's economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, ...


Bounds on performance for multiple target tracking

F. E. Daum IEEE Transactions on Automatic Control, 1990

A theoretical lower bound on mean-square-estimation error is derived for tracking in dense multiple-target environments. This family of bounds is computationally tractable, because it does not require computing the optimal estimate. Computational complexity can be traded for tightness of the lower bounds by varying the number of hypotheses considered. The theory can be used to study the fundamental limitations of ...


Robust layered sensing: From sparse signals to sparse residuals

Vassilis Kekatos; Georgios B. Giannakis 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010

One of the key challenges in sensing networks is the extraction of information by fusing data from a multitude of possibly unreliable sensors. Robust sensing, viewed here as the simultaneous recovery of the wanted information- bearing signal vector together with the subset of (un)reliable sensors, is a problem whose optimum solution incurs combinatorial complexity. The present paper relaxes this problem ...


Tracking algorithm designed by the local asymptotic approach

E. Wahnon; N. Berman IEEE Transactions on Automatic Control, 1990

The problem of sequential detection of parameter jumps in linear systems with constant noise level is discussed. The detection problem is analyzed by the asymptotic local approach, using the normalized output error sequence as the detection signal. For linear regression, ARMAX, and state-space models, a central limit theorem is proved, transforming the original problem into the problem of detecting an ...


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Educational Resources on Linear regression

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eLearning

Dialect/Accent Classification Using Unrestricted Audio

Rongqing Huang; John H. L. Hansen; Pongtep Angkititrakul IEEE Transactions on Audio, Speech, and Language Processing, 2007

This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the ...


Agriculture yield prediction using predictive analytic techniques

S. Nagini; T. V. Rajini Kanth; B. V. Kiranmayee 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 2016

India's economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, ...


Bounds on performance for multiple target tracking

F. E. Daum IEEE Transactions on Automatic Control, 1990

A theoretical lower bound on mean-square-estimation error is derived for tracking in dense multiple-target environments. This family of bounds is computationally tractable, because it does not require computing the optimal estimate. Computational complexity can be traded for tightness of the lower bounds by varying the number of hypotheses considered. The theory can be used to study the fundamental limitations of ...


Robust layered sensing: From sparse signals to sparse residuals

Vassilis Kekatos; Georgios B. Giannakis 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010

One of the key challenges in sensing networks is the extraction of information by fusing data from a multitude of possibly unreliable sensors. Robust sensing, viewed here as the simultaneous recovery of the wanted information- bearing signal vector together with the subset of (un)reliable sensors, is a problem whose optimum solution incurs combinatorial complexity. The present paper relaxes this problem ...


Tracking algorithm designed by the local asymptotic approach

E. Wahnon; N. Berman IEEE Transactions on Automatic Control, 1990

The problem of sequential detection of parameter jumps in linear systems with constant noise level is discussed. The detection problem is analyzed by the asymptotic local approach, using the normalized output error sequence as the detection signal. For linear regression, ARMAX, and state-space models, a central limit theorem is proved, transforming the original problem into the problem of detecting an ...


More eLearning Resources

IEEE-USA E-Books

  • Regression Estimation

    This chapter contains sections titled: Linear Regression with Insensitive Loss function, Dual Problems, -SV Regression, Convex Combinations and 1-Norms, Parametric Insensitivity Models, Applications, Summary, Problems

  • No title

    There are many books written about statistics, some brief, some detailed, some humorous, some colorful, and some quite dry. Each of these texts is designed for a specific audience. Too often, texts about statistics have been rather theoretical and intimidating for those not practicing statistical analysis on a routine basis. Thus, many engineers and scientists, who need to use statistics much more frequently than calculus or differential equations, lack sufficient knowledge of the use of statistics. The audience that is addressed in this text is the university-level biomedical engineering student who needs a bare-bones coverage of the most basic statistical analysis frequently used in biomedical engineering practice. The text introduces students to the essential vocabulary and basic concepts of probability and statistics that are required to perform the numerical summary and statistical analysis used in the biomedical field. This text is considered a starting point for important issue to consider when designing experiments, summarizing data, assuming a probability model for the data, testing hypotheses, and drawing conclusions from sampled data. A student who has completed this text should have sufficient vocabulary to read more advanced texts on statistics and further their knowledge about additional numerical analyses that are used in the biomedical engineering field but are beyond the scope of this text. This book is designed to supplement an undergraduate-level course in applied statistics, specifically in biomedical engineering. Practicing engineers who have not had formal instruction in statistics may also use this text as a simple, brief introduction to statistics used in biomedical engineering. The emphasis is on the application of statistics, the assumptions made in applying the statistical tests, the limitations of these elementary statistical methods, and the errors often committed in using statistical analysis. A number of examples from biomedical engi eering research and industry practice are provided to assist the reader in understanding concepts and application. It is beneficial for the reader to have some background in the life sciences and physiology and to be familiar with basic biomedical instrumentation used in the clinical environment. Contents: Introduction / Collecting Data and Experimental Design / Data Summary and Descriptive Statistics / Assuming a Probability Model from the Sample Data / Statistical Inference / Linear Regression and Correlation Analysis / Power Analysis and Sample Size / Just the Beginning / Bibliography

  • A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift

    This chapter contains sections titled: Conditional Expectation Analysis of Generalization Error, Linear Regression under Covariate Shift, Model Selection, Active Learning, Active Learning with Model Selection, Conclusions

  • Generalization of Linear Regression Problems

    This chapter contains sections titled: Introduction Generalized Total Least Squares (GeTLS EXIN) Approach GeTLS Stability Analysis Neural Nongeneric Unidimensional TLS Scheduling Accelerated MCA EXIN Neuron (MCA EXIN+) Further Considerations Simulations for the GeTLS EXIN Neuron

  • Complements and Details

    This chapter contains sections titled: Optimum Gains for Recursive Linear Regression, "Quick and Dirty" Recursive Linear Regression, Optimum Gains for Recursive Linear Regression. Batch Processing, "Quick and Dirty" Linear Regression. Batch Processing, Gain Sequences for Recursive Nonlinear Regression. The Method of Linearization, Sufficient Conditions for Assumptions E1 ThroughE E6′ (E6) When the Gains (Equations 7.48) Are Used, Limitations of the Recursive Method. III Conditioning, Response Surfaces

  • Prediction in Probability Spaces

    This chapter contains sections titled: Conditional Distribution, Regression on a Single Variable, Regression on a Partition or a Family of Variables, Linear Regression on a Single Variable, Linear Regression on a Family of Variables

  • Multiple Regression and Model Building

    Multiple regression, where more than one predictor variable is used to estimate a response variable, is introduced by way of an example. To allow for inference, the multiple regression model is defined, with both model and inferential methods representing extensions of the simple linear regression case. Next, regression with categorical predictors (indicator variables) is explained. The problems of multicollinearity are examined; multicollinearity represents an unstable response surface due to overly correlated predictors. The variance inflation factor is defined, as an aid in identifying multicollinear predictors. Variable selection methods are then provided, including forward selection, backward elimination, stepwise, and best-subsets regression. Mallows'C p statistic is defined, as an aid in variable selection. Finally, methods for using the principal components as predictors in multiple regression are discussed.

  • Joint Kernel Maps

    This chapter contains sections titled: Introduction, Incorporating Correlations into Linear Regression, Linear Maps and Kernel Methods : Generalizing Support Vector Machines, Joint Kernel Maps, Joint Kernel, Experiments, Conclusions

  • Linear Regression

    This chapter contains sections titled: 12.1 Introduction, 12.2 Least-Squares Estimation, 12.3 The Linear Model, 12.4 Universal Models for Linear Regression

  • High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression

    We focus on the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on ℓ1-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an ℓ1-constraint. Our framework applies to the high-dimensional setting, in which both the number of nodes p and maximum neighborhood sizes d are allowed to grow as a function of the number of observations n. Our main result is to establish sufficient conditions on the triple (n, p, d) for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. Under certain mutual incoherence conditions analogous to those imposed in previous work on linear regression, we prove that consistent neighborhood selection can be obtained as long as the number of observations n grows more quickly than 6d6 log d + 2d5 log p, thereby establishing that logarithmic growth in the number of samples n relative to graph size p is sufficient to achieve neighborhood consistency.



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