Linear regression
7,087 resources related to Linear regression
IEEE Organizations related to Linear regression
Back to TopConferences related to Linear regression
Back to Top2012 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, knowledgebased 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 interdisciplinary applications.
Periodicals related to Linear regression
Back to TopAudio, 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
Signalprocessing 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 halftoning 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
Back to TopA 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 aposteriori (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 outofclass data. In this paper, we consider the speaker verification task using ...
A Semidefinite 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 costbenefit 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 costbenefit to stakeholders. A postal questionnaire was distributed to 100 client and contractor organizations in Australia in 1997. Fortyeight 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
Back to TopeLearning
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 aposteriori (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 outofclass data. In this paper, we consider the speaker verification task using ...
A Semidefinite 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 costbenefit 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 costbenefit to stakeholders. A postal questionnaire was distributed to 100 client and contractor organizations in Australia in 1997. Fortyeight 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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IEEEUSA EBooks

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

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 leastsquares 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 returnbased 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 leastsquares linear regression is used in an intensity modulated radiation therapy (IMRT) treatmentplanning problem. Robust regression aims to reduce influence of sample outliers on regression parameters, especially when regression error has heavy tails.

This chapter contains sections titled: 12.1 Introduction, 12.2 LeastSquares 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

MapReduce 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 mapreduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), kmeans, 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 TransitionPolarity Detection Conclusions References Editor Comments

HighDimensional Graphical Model Selection Using ℓ1Regularized 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 ℓ1regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an ℓ1constraint. Our framework applies to the highdimensional 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.
Standards related to Linear regression
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