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 ...



Most published Xplore authors for Linear regression

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Xplore Articles related to Linear regression

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Why a nonlinear solution for a linear problem? [channel equalization]

T. Adali Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), 1999

We emphasize a key point that when there is noise in the system, even if the system is linear, a nonlinear solution is more desirable. We derive a simple expression that shows that for a linear regression model, the logistic nonlinearity will be the natural match for modeling posterior class probabilities, and that the steepness of this logistic function is ...


Fetal heart rate pattern in prenatal diagnosis - fetal autonomic brain age score

Dirk Hoyer; Uwe Schneider Computing in Cardiology 2013, 2013

Fetal heart rate patterns provide valuable information about normal fetal maturation. For heart rate variability (HRV) analysis to be successful in prenatal diagnosis the selection of appropriate HRV indices is required. Those indices were organized according to universal principles of developmental biology. Key characteristics of evolution and self-organization are increasing fluctuation amplitude, increasing complexity and pattern formation. Related HRV indices ...


Combining feature sets with support vector machines: application to speaker recognition

A. O. Hatch; A. Stolcke; B. Peskin IEEE Workshop on Automatic Speech Recognition and Understanding, 2005., 2005

In this paper, we describe a general technique for optimizing the relative weights of feature sets in a support vector machine (SVM) and show how it can be applied to the field of speaker recognition. Our training procedure uses an objective function that maps the relative weights of the feature sets directly to a classification metric (e.g. equal-error rate (EER)) ...


Ventilatory threshold prediction by spectral analysis of heart rate variability in incremental maximal tests

A. Benítez; M A García-González; R. Angulo; F. Rodríguez; X. Iglesias; R. Bescós; M. Marina; J M Padullés 2010 Computing in Cardiology, 2010

Ventilatory thresholds (VT1 and VT2) are useful in many fields of medicine and sports. Nevertheless, their measurement is cumbersome and needs trained personnel. This work proposes an alternative method to predict VT1, VT2 and maximum loads in incremental maximal tests based on heart rate variability (HRV) analysis. Twelve competitive male cyclists executed an incremental exhaustive test. During the test, RR ...


Using Linear Regression Analysis for Face Recognition Based on PCA and LDA

Gang Xu; Shengli Zhang; Yunyun Liang 2009 International Conference on Computational Intelligence and Software Engineering, 2009

Aiming at pose-invariant face recognition, this paper proposes a method of generating virtual frontal view from a non-frontal face image, we use the virtual frontal face image instead of the input non-frontal image as the test image with the exertion of principal component analysis (PCA) and linear discriminant analysis (LDA) to finally achieve face recognition procedure. In this paper, this ...


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Educational Resources on Linear regression

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eLearning

Why a nonlinear solution for a linear problem? [channel equalization]

T. Adali Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), 1999

We emphasize a key point that when there is noise in the system, even if the system is linear, a nonlinear solution is more desirable. We derive a simple expression that shows that for a linear regression model, the logistic nonlinearity will be the natural match for modeling posterior class probabilities, and that the steepness of this logistic function is ...


Fetal heart rate pattern in prenatal diagnosis - fetal autonomic brain age score

Dirk Hoyer; Uwe Schneider Computing in Cardiology 2013, 2013

Fetal heart rate patterns provide valuable information about normal fetal maturation. For heart rate variability (HRV) analysis to be successful in prenatal diagnosis the selection of appropriate HRV indices is required. Those indices were organized according to universal principles of developmental biology. Key characteristics of evolution and self-organization are increasing fluctuation amplitude, increasing complexity and pattern formation. Related HRV indices ...


Combining feature sets with support vector machines: application to speaker recognition

A. O. Hatch; A. Stolcke; B. Peskin IEEE Workshop on Automatic Speech Recognition and Understanding, 2005., 2005

In this paper, we describe a general technique for optimizing the relative weights of feature sets in a support vector machine (SVM) and show how it can be applied to the field of speaker recognition. Our training procedure uses an objective function that maps the relative weights of the feature sets directly to a classification metric (e.g. equal-error rate (EER)) ...


Ventilatory threshold prediction by spectral analysis of heart rate variability in incremental maximal tests

A. Benítez; M A García-González; R. Angulo; F. Rodríguez; X. Iglesias; R. Bescós; M. Marina; J M Padullés 2010 Computing in Cardiology, 2010

Ventilatory thresholds (VT1 and VT2) are useful in many fields of medicine and sports. Nevertheless, their measurement is cumbersome and needs trained personnel. This work proposes an alternative method to predict VT1, VT2 and maximum loads in incremental maximal tests based on heart rate variability (HRV) analysis. Twelve competitive male cyclists executed an incremental exhaustive test. During the test, RR ...


Using Linear Regression Analysis for Face Recognition Based on PCA and LDA

Gang Xu; Shengli Zhang; Yunyun Liang 2009 International Conference on Computational Intelligence and Software Engineering, 2009

Aiming at pose-invariant face recognition, this paper proposes a method of generating virtual frontal view from a non-frontal face image, we use the virtual frontal face image instead of the input non-frontal image as the test image with the exertion of principal component analysis (PCA) and linear discriminant analysis (LDA) to finally achieve face recognition procedure. In this paper, this ...


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IEEE-USA E-Books

  • The Minimum Description Length Principle in Coding and Modeling

    We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon's basic source coding theorem. The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to within asymptotically vanishing terms. We assess the performance of the minimum description length criterion both from the vantage point of quality of data compression and accuracy of statistical inference. Context tree modeling, density estimation, and model selection in Gaussian linear regression serve as examples.

  • 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

  • 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.

  • 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.

  • MDL Model Selection

    This chapter contains sections titled: 14.1 Introduction, 14.2 Simple Refined MDL Model Selection, 14.3 General Parametric Model Selection, 14.4 Practical Issues in MDL Model Selection, 14.5 MDL Model Selection for Linear Regression, 14.6 Worst Case vs. Average Case

  • 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

  • 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

  • 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

  • Slope Filtering: An FIR Approach to Linear Regression

    This chapter contains sections titled: Motivation for Slope Filtering Linear Regression Application: Receiver Carrier Recovery Application: Signal Rate of Change Estimation Application: Signal Transition Detection Application: Signal Transition-Polarity Detection Conclusions References Editor Comments

  • Regression Modeling

    Chapter two begins by using an example to introduce simple linear regression and the concept of least squares. The usefulness of the regression is then measured by the coefficient of determination r 2, and the typical prediction error is estimated using the standard error of the estimate s. The correlation coefficient r is discussed, along with the ANOVA table for succinct display of results. Outliers, high leverage points, and influential observations are discussed in detail. Moving from descriptive methods to inference, the regression model is introduced. The t-test for the relationship between x and y is shown, along with the confidence interval for the slope of the regression line, the confidence interval for the mean value of y given x, and the prediction interval for a randomly chosen value of y given x. Methods are shown for verifying the assumptions underlying the regression model. Detailed examples are provided using the Baseball and California data sets. Finally, methods of applying transformations to achieve linearity is provided.



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