Linear regression
6,959 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 TopDialect/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 wordbased 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 Wordbased Dialect Classification (WDC), converts the textindependent decision problem into a textdependent decision problem and produces multiple combination decisions at the ...
Bounds on performance for multiple target tracking
F. E. Daum IEEE Transactions on Automatic Control, 1990
A theoretical lower bound on meansquareestimation error is derived for tracking in dense multipletarget 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 statespace models, a central limit theorem is proved, transforming the original problem into the problem of detecting an ...
Prediction of Infectious Disease Spread Using Twitter: A Case of Influenza
Hideo Hirose; Liangliang Wang 2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming, 2012
Nowadays, detecting the disaster phenomena and predicting the final stage become very important in the risk analysis viewpoint. The statistical methods provide accurate estimates of parameters when the data are completely given. However, when the data are incomplete, the accuracy of the estimates becomes poor. Therefore, statistical methods are weak in predicting the future trends. The SIR methods, for infectious ...
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Educational Resources on Linear regression
Back to TopeLearning
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 wordbased 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 Wordbased Dialect Classification (WDC), converts the textindependent decision problem into a textdependent decision problem and produces multiple combination decisions at the ...
Bounds on performance for multiple target tracking
F. E. Daum IEEE Transactions on Automatic Control, 1990
A theoretical lower bound on meansquareestimation error is derived for tracking in dense multipletarget 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 statespace models, a central limit theorem is proved, transforming the original problem into the problem of detecting an ...
Prediction of Infectious Disease Spread Using Twitter: A Case of Influenza
Hideo Hirose; Liangliang Wang 2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming, 2012
Nowadays, detecting the disaster phenomena and predicting the final stage become very important in the risk analysis viewpoint. The statistical methods provide accurate estimates of parameters when the data are completely given. However, when the data are incomplete, the accuracy of the estimates becomes poor. Therefore, statistical methods are weak in predicting the future trends. The SIR methods, for infectious ...
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IEEEUSA EBooks

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

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 universitylevel biomedical engineering student who needs a barebones 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 undergraduatelevel 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

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 bestsubsets 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.

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

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

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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