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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A Bayesian perspective on Residential Demand Response using smart meter data

Datong Zhou; Maximilian Balandat; Claire Tomlin 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016

The widespread deployment of Advanced Metering Infrastructure has made granular data of residential electricity consumption available on a large scale. One field of research that relies on such granular consumption data is Residential Demand Response, where individual users are incentivized to temporarily reduce their consumption during periods of high marginal cost of electricity. To quantify the economic potential of Residential ...


Comparison of discriminative training methods for speaker verification

Chengyuan Ma; E. Chang Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on, 2003

The maximum likelihood estimation (MLE) and Bayesian maximum a-posteriori (MAP) adaptation methods for Gaussian mixture models (GMM) have proven to be effective and efficient for speaker verification, even though each speaker model is trained using only his own training utterances. Discriminative criteria aim at increasing discriminability by using out-of-class data. In this paper, we consider the speaker verification task using ...


A Semi-definite Positive Linear Discriminant Analysis and Its Applications

Deguang Kong; Chris Ding 2012 IEEE 12th International Conference on Data Mining, 2012

Linear Discriminant Analysis (LDA) is widely used for dimension reduction in classification tasks. However, standard LDA formulation is not semi definite positive (s.d.p), and thus it is difficult to obtain the global optimal solution when standard LDA formulation is combined with other loss functions or graph embedding. In this paper, we present an alternative approach to LDA. We rewrite the ...


Contractor selection criteria: a cost-benefit analysis

S. T. Ng; R. M. Skitmore IEEE Transactions on Engineering Management, 2001

This paper describes an empirical study aimed at ranking prequalification criteria on the basis of perceived total cost-benefit to stakeholders. A postal questionnaire was distributed to 100 client and contractor organizations in Australia in 1997. Forty-eight responses were analyzed for scores on 38 categories of contractor information in terms of "value to client" "contractor costs," "client costs," and "value for ...


Robust face recognition using trimmed linear regression

Jian Lai; Xudong Jiang 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013

In this work, we focus on the problem of partially occluded face recognition. Using a robust estimator, we detect and trim the contaminated pixels from query sample. The corresponding pixels in the training samples are trimmed as well. The linear regression is applied to the trimmed images. Finally, the query image is labeled to the class with minimum normalized reconstruction ...


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Educational Resources on Linear regression

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eLearning

A Bayesian perspective on Residential Demand Response using smart meter data

Datong Zhou; Maximilian Balandat; Claire Tomlin 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016

The widespread deployment of Advanced Metering Infrastructure has made granular data of residential electricity consumption available on a large scale. One field of research that relies on such granular consumption data is Residential Demand Response, where individual users are incentivized to temporarily reduce their consumption during periods of high marginal cost of electricity. To quantify the economic potential of Residential ...


Comparison of discriminative training methods for speaker verification

Chengyuan Ma; E. Chang Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on, 2003

The maximum likelihood estimation (MLE) and Bayesian maximum a-posteriori (MAP) adaptation methods for Gaussian mixture models (GMM) have proven to be effective and efficient for speaker verification, even though each speaker model is trained using only his own training utterances. Discriminative criteria aim at increasing discriminability by using out-of-class data. In this paper, we consider the speaker verification task using ...


A Semi-definite Positive Linear Discriminant Analysis and Its Applications

Deguang Kong; Chris Ding 2012 IEEE 12th International Conference on Data Mining, 2012

Linear Discriminant Analysis (LDA) is widely used for dimension reduction in classification tasks. However, standard LDA formulation is not semi definite positive (s.d.p), and thus it is difficult to obtain the global optimal solution when standard LDA formulation is combined with other loss functions or graph embedding. In this paper, we present an alternative approach to LDA. We rewrite the ...


Contractor selection criteria: a cost-benefit analysis

S. T. Ng; R. M. Skitmore IEEE Transactions on Engineering Management, 2001

This paper describes an empirical study aimed at ranking prequalification criteria on the basis of perceived total cost-benefit to stakeholders. A postal questionnaire was distributed to 100 client and contractor organizations in Australia in 1997. Forty-eight responses were analyzed for scores on 38 categories of contractor information in terms of "value to client" "contractor costs," "client costs," and "value for ...


Robust face recognition using trimmed linear regression

Jian Lai; Xudong Jiang 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013

In this work, we focus on the problem of partially occluded face recognition. Using a robust estimator, we detect and trim the contaminated pixels from query sample. The corresponding pixels in the training samples are trimmed as well. The linear regression is applied to the trimmed images. Finally, the query image is labeled to the class with minimum normalized reconstruction ...


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IEEE-USA E-Books

  • 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

  • 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

  • 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.

  • 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

  • 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.

  • 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

  • Map-Reduce for Machine Learning on Multicore

    We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain 'summation form,' which allows them to be easily parallelized on multicore computers. We adapt Google's map-reduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.

  • 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

  • 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

  • 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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