10/2018, 1, ISBN 9781119501398, 721

Provides a modern approach to least squares estimation and data analysis for undergraduate land surveying and geomatics programs Rich in theory and concepts,...

Estimation theory | Least squares

Estimation theory | Least squares

eBook

IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 01/2012, Volume 23, Issue 1, pp. 22 - 32

In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel...

Kernel methods | mean square convergence | Energy conservation | Quantization | vector quantization | Vectors | Steady-state | quantized kernel least mean square | Kernel | Least squares approximation | Convergence | DESIGN | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | ADAPTIVE FILTERS | PERFORMANCE | NONLINEARITIES | STATE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | TRANSIENT ANALYSIS | ENGINEERING, ELECTRICAL & ELECTRONIC | LMS | ERROR | COMPUTER SCIENCE, THEORY & METHODS | ENTROPY | Technology application | Monte Carlo method | Usage | Kernel functions | Analysis | Gaussian processes | Least squares | Machine learning | Studies | Mean square errors

Kernel methods | mean square convergence | Energy conservation | Quantization | vector quantization | Vectors | Steady-state | quantized kernel least mean square | Kernel | Least squares approximation | Convergence | DESIGN | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | ADAPTIVE FILTERS | PERFORMANCE | NONLINEARITIES | STATE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | TRANSIENT ANALYSIS | ENGINEERING, ELECTRICAL & ELECTRONIC | LMS | ERROR | COMPUTER SCIENCE, THEORY & METHODS | ENTROPY | Technology application | Monte Carlo method | Usage | Kernel functions | Analysis | Gaussian processes | Least squares | Machine learning | Studies | Mean square errors

Journal Article

2017, A Princeton lifesaver study guide, ISBN 9780691149547, xxiii, 727 pages

The essential lifesaver for students who want to master probability For students learning probability, its numerous applications, techniques, and methods can...

Games of chance (Mathematics) | Random variables | Probabilities | Chance | MATHEMATICS | Probability & Statistics | General | Education | Statistics

Games of chance (Mathematics) | Random variables | Probabilities | Chance | MATHEMATICS | Probability & Statistics | General | Education | Statistics

Book

Journal of Statistical Software, ISSN 1548-7660, 2016, Volume 69, Issue 1, pp. 1 - 33

Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects...

Experimental design | Linear models | Least-squares means | experimental design | STATISTICS & PROBABILITY | least-squares means | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | LINEAR MIXED MODELS | linear models

Experimental design | Linear models | Least-squares means | experimental design | STATISTICS & PROBABILITY | least-squares means | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | LINEAR MIXED MODELS | linear models

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 05/2012, Volume 60, Issue 5, pp. 2223 - 2235

As one of the recently proposed algorithms for sparse system identification, l 0 norm constraint Least Mean Square ( l 0 -LMS) algorithm modifies the cost...

steady-state misalignment | LMS | white Gaussian signal | sparse system identification | Adaptive filter | convergence rate | independence assumption | mean square deviation | performance analysis | Usage | Series, Taylor's | Least squares | Innovations | Signal processing | Approximation theory | Simulation methods | Methods | Algorithms | Approximation | Least mean squares algorithm | Norms | Taylor series | Gaussian | Deviation | Convergence

steady-state misalignment | LMS | white Gaussian signal | sparse system identification | Adaptive filter | convergence rate | independence assumption | mean square deviation | performance analysis | Usage | Series, Taylor's | Least squares | Innovations | Signal processing | Approximation theory | Simulation methods | Methods | Algorithms | Approximation | Least mean squares algorithm | Norms | Taylor series | Gaussian | Deviation | Convergence

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 02/2008, Volume 56, Issue 2, pp. 543 - 554

The combination of the famed kernel trick and the least-mean-square (LMS) algorithm provides an interesting sample-by-sample update for an adaptive filter in...

Algorithm design and analysis | Machine learning algorithms | Adaptive filters | least mean square | Least squares approximation | Tikhonov regularization | Radio access networks | Kernel methods | Signal processing algorithms | Training data | Hilbert space | Kernel | Principal component analysis | Least mean square | STABILITY | kernel methods | NETWORKS | COMPONENT ANALYSIS | REGULARIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Kernels | Algorithms | Acoustic filters | Analysis | Studies | Computational mathematics | Learning | Mathematical analysis | Signal processing | Mathematical models | Regularization

Algorithm design and analysis | Machine learning algorithms | Adaptive filters | least mean square | Least squares approximation | Tikhonov regularization | Radio access networks | Kernel methods | Signal processing algorithms | Training data | Hilbert space | Kernel | Principal component analysis | Least mean square | STABILITY | kernel methods | NETWORKS | COMPONENT ANALYSIS | REGULARIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Kernels | Algorithms | Acoustic filters | Analysis | Studies | Computational mathematics | Learning | Mathematical analysis | Signal processing | Mathematical models | Regularization

Journal Article

IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 02/2014, Volume 25, Issue 2, pp. 265 - 277

The multikernel least-mean-square algorithm is introduced for adaptive estimation of vector-valued nonlinear and nonstationary signals. This is achieved by...

Algorithm design and analysis | Least squares approximations | least mean square (LMS) | Estimation | vector RKHS | multiple kernels | Training | wind prediction | Adaptive sparsification | kernel methods | Approximation algorithms | Hilbert space | Kernel | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | COMPUTER SCIENCE, THEORY & METHODS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Kernel functions | Signal processing | Analysis

Algorithm design and analysis | Least squares approximations | least mean square (LMS) | Estimation | vector RKHS | multiple kernels | Training | wind prediction | Adaptive sparsification | kernel methods | Approximation algorithms | Hilbert space | Kernel | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | COMPUTER SCIENCE, THEORY & METHODS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Kernel functions | Signal processing | Analysis

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 05/2008, Volume 56, Issue 5, pp. 1853 - 1864

This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS) adaptive filters that simultaneously adapt...

Algorithm design and analysis | stochastic algorithms | Adaptive algorithm | convex combination | least mean square (LMS) | Adaptive filters | Stochastic processes | Resonance light scattering | Steady-state | analysis | Least squares approximation | Convergence | Performance analysis | affine combination | Convex combination | Stochastic algorithms | Least mean square (LMS) | Affine combination | Analysis | adaptive filters | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Evaluation | Usage | Gaussian processes | Least squares | Studies | Engineering Sciences | Signal and Image Processing | Computer Science

Algorithm design and analysis | stochastic algorithms | Adaptive algorithm | convex combination | least mean square (LMS) | Adaptive filters | Stochastic processes | Resonance light scattering | Steady-state | analysis | Least squares approximation | Convergence | Performance analysis | affine combination | Convex combination | Stochastic algorithms | Least mean square (LMS) | Affine combination | Analysis | adaptive filters | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Evaluation | Usage | Gaussian processes | Least squares | Studies | Engineering Sciences | Signal and Image Processing | Computer Science

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 11/2011, Volume 59, Issue 11, pp. 5225 - 5235

Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical...

Algorithm design and analysis | nonnegative constraints | adaptive signal processing | Adaptive filters | Prediction algorithms | transient analysis | Facsimile | least mean square algorithms | Mathematical model | Least squares approximation | Equations | Convergence | SIGNAL | PROJECTED GRADIENT METHODS | CONSTRAINTS | SYSTEMS | MATRIX FACTORIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Usage | Dynamical systems | Simulation methods | Innovations | Least squares | Algorithms | Dynamics | Consistency | Estimates | Descent

Algorithm design and analysis | nonnegative constraints | adaptive signal processing | Adaptive filters | Prediction algorithms | transient analysis | Facsimile | least mean square algorithms | Mathematical model | Least squares approximation | Equations | Convergence | SIGNAL | PROJECTED GRADIENT METHODS | CONSTRAINTS | SYSTEMS | MATRIX FACTORIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Usage | Dynamical systems | Simulation methods | Innovations | Least squares | Algorithms | Dynamics | Consistency | Estimates | Descent

Journal Article

10.
Full Text
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis

IEEE Transactions on Signal Processing, ISSN 1053-587X, 07/2008, Volume 56, Issue 7, pp. 3122 - 3136

We formulate and study distributed estimation algorithms based on diffusion protocols to implement cooperation among individual adaptive nodes. The individual...

distributed estimation | Adaptive systems | Protocols | Parameter estimation | Peer to peer computing | Adaptive filters | Closed-form solution | Steady-state | Degradation | Adaptive networks | consensus | Distributed processing | diffusion algorithm | Performance analysis | cooperation | Diffusion algorithm | Distributed estimation | Cooperation | Consensus | adaptive networks | distributed processing | AGENTS | CONVEX COMBINATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Usage | Research | Analysis | Least squares | Learning | Networks | Errors | Algorithms | Mathematical analysis | Exact solutions | Protocol (computers) | Diffusion

distributed estimation | Adaptive systems | Protocols | Parameter estimation | Peer to peer computing | Adaptive filters | Closed-form solution | Steady-state | Degradation | Adaptive networks | consensus | Distributed processing | diffusion algorithm | Performance analysis | cooperation | Diffusion algorithm | Distributed estimation | Cooperation | Consensus | adaptive networks | distributed processing | AGENTS | CONVEX COMBINATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Usage | Research | Analysis | Least squares | Learning | Networks | Errors | Algorithms | Mathematical analysis | Exact solutions | Protocol (computers) | Diffusion

Journal Article

Signal Processing, ISSN 0165-1684, 11/2012, Volume 92, Issue 11, pp. 2624 - 2632

In this paper, we study the mean square convergence of the kernel least mean square (KLMS). The fundamental energy conservation relation has been established...

Kernel adaptive filter | Kernel least mean square | Mean square convergence | Energy conservation relation | ADAPTIVE FILTERS | TRANSIENT ANALYSIS | ENGINEERING, ELECTRICAL & ELECTRONIC | Monte Carlo method | Algorithms | Energy conservation | Analysis | Kernels | Mean square values | Monte Carlo methods | Computer simulation | Least mean squares algorithm | Optimization | Convergence

Kernel adaptive filter | Kernel least mean square | Mean square convergence | Energy conservation relation | ADAPTIVE FILTERS | TRANSIENT ANALYSIS | ENGINEERING, ELECTRICAL & ELECTRONIC | Monte Carlo method | Algorithms | Energy conservation | Analysis | Kernels | Mean square values | Monte Carlo methods | Computer simulation | Least mean squares algorithm | Optimization | Convergence

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 01/2011, Volume 59, Issue 1, pp. 130 - 144

In this paper, Part II of a two-part study, we derive a closed-form analytic representation of the Bayesian minimum mean-square error (MMSE) error estimator...

error estimation | Correlation | Bayesian estimation | Gaussian distribution | Closed-form solution | Covariance matrix | classification | Analytical models | Bayesian methods | linear classification | minimum-mean-square estimation | genomics | Robustness | small samples | ENGINEERING, ELECTRICAL & ELECTRONIC | Robust statistics | Bayesian statistical decision theory | Technology application | Usage | Analysis | Gaussian processes | Least squares | Innovations | Signal processing | Distribution (Probability theory) | Hessian matrices | Methods | Permissible error | Error analysis | Covariance | Mathematical analysis | Classification | Exact solutions | Mathematical models | Gaussian | Estimators

error estimation | Correlation | Bayesian estimation | Gaussian distribution | Closed-form solution | Covariance matrix | classification | Analytical models | Bayesian methods | linear classification | minimum-mean-square estimation | genomics | Robustness | small samples | ENGINEERING, ELECTRICAL & ELECTRONIC | Robust statistics | Bayesian statistical decision theory | Technology application | Usage | Analysis | Gaussian processes | Least squares | Innovations | Signal processing | Distribution (Probability theory) | Hessian matrices | Methods | Permissible error | Error analysis | Covariance | Mathematical analysis | Classification | Exact solutions | Mathematical models | Gaussian | Estimators

Journal Article

01/2017, ISBN 1482253356

Book Chapter

Journal of Parallel and Distributed Computing, ISSN 0743-7315, 2007, Volume 67, Issue 1, pp. 33 - 46

We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed...

Edge-transitive graphs | Convex optimization | Distributed average consensus | Least-mean-square | least-mean-square | distributed average consensus | EIGENVALUES | edge-transitive graphs | MULTIAGENT SYSTEMS | convex optimization | DIFFUSION | AGENTS | COMPUTER SCIENCE, THEORY & METHODS | SUMS

Edge-transitive graphs | Convex optimization | Distributed average consensus | Least-mean-square | least-mean-square | distributed average consensus | EIGENVALUES | edge-transitive graphs | MULTIAGENT SYSTEMS | convex optimization | DIFFUSION | AGENTS | COMPUTER SCIENCE, THEORY & METHODS | SUMS

Journal Article

15.
Full Text
Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis

IEEE Transactions on Signal Processing, ISSN 1053-587X, 09/2010, Volume 58, Issue 9, pp. 4795 - 4810

This paper presents an efficient adaptive combination strategy for the distributed estimation problem over diffusion networks in order to improve robustness...

distributed estimation | Adaptive systems | diffusion | Statistical analysis | Stability analysis | Steady-state | Adaptive filter | Least squares approximation | distributed algorithm | adaptive networks | Statistical distributions | energy conservation | Approximation algorithms | Performance analysis | Noise robustness | Signal to noise ratio | combination | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | Technology application | Usage | Innovations | Least squares | Signal processing | Distributed processing (Computers) | Methods | Simulation methods | Networks | Algorithms | Approximation | Adaptive algorithms | Strategy | Robustness | Diffusion | Statistics

distributed estimation | Adaptive systems | diffusion | Statistical analysis | Stability analysis | Steady-state | Adaptive filter | Least squares approximation | distributed algorithm | adaptive networks | Statistical distributions | energy conservation | Approximation algorithms | Performance analysis | Noise robustness | Signal to noise ratio | combination | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | Technology application | Usage | Innovations | Least squares | Signal processing | Distributed processing (Computers) | Methods | Simulation methods | Networks | Algorithms | Approximation | Adaptive algorithms | Strategy | Robustness | Diffusion | Statistics

Journal Article

IEEE Signal Processing Letters, ISSN 1070-9908, 12/2007, Volume 14, Issue 12, pp. 988 - 991

An interference-normalized least mean square (INLMS) algorithm for robust adaptive filtering is proposed. The INLMS algorithm extends the gradient-adaptive...

Echo cancellers | Additives | normalized least mean square (NLMS) algorithm | Adaptive filters | Stochastic processes | Interference | Filtering algorithms | Adaptive filtering | Robustness | gradient-adaptive learning rate | Least mean square algorithms | Convergence | Learning systems | Gradient-adaptive learning rate | Gradient analysis | Least mean squares method | Normalized least mean square (NLMS) algorithm | adaptive filtering | ADAPTIVE STEP-SIZE | ENGINEERING, ELECTRICAL & ELECTRONIC | Learning | Algorithms | Least mean squares | Least mean squares algorithm | Signal processing

Echo cancellers | Additives | normalized least mean square (NLMS) algorithm | Adaptive filters | Stochastic processes | Interference | Filtering algorithms | Adaptive filtering | Robustness | gradient-adaptive learning rate | Least mean square algorithms | Convergence | Learning systems | Gradient-adaptive learning rate | Gradient analysis | Least mean squares method | Normalized least mean square (NLMS) algorithm | adaptive filtering | ADAPTIVE STEP-SIZE | ENGINEERING, ELECTRICAL & ELECTRONIC | Learning | Algorithms | Least mean squares | Least mean squares algorithm | Signal processing

Journal Article

Circuits, Systems, and Signal Processing, ISSN 0278-081X, 10/2019, Volume 38, Issue 10, pp. 4817 - 4839

In this work, a new class of stochastic gradient algorithm is developed based on q-calculus. Unlike the existing q-LMS algorithm, the proposed approach fully...

Engineering | Signal,Image and Speech Processing | Jackson derivative | Adaptive algorithms | Least mean squares algorithm | q -calculus | Electronics and Microelectronics, Instrumentation | q -LMS | Circuits and Systems | System identification | Electrical Engineering | LMS ALGORITHM | DERIVATION | CONVERGENCE | q-LMS | q-calculus | FAMILY | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Analysis | Algorithms | Errors | Computer simulation | Mathematical analysis | Machine learning | Convergence

Engineering | Signal,Image and Speech Processing | Jackson derivative | Adaptive algorithms | Least mean squares algorithm | q -calculus | Electronics and Microelectronics, Instrumentation | q -LMS | Circuits and Systems | System identification | Electrical Engineering | LMS ALGORITHM | DERIVATION | CONVERGENCE | q-LMS | q-calculus | FAMILY | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Analysis | Algorithms | Errors | Computer simulation | Mathematical analysis | Machine learning | Convergence

Journal Article

Neurocomputing, ISSN 0925-2312, 05/2016, Volume 191, pp. 95 - 106

Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is usually...

Kernel methods | Kernel least mean square | Kernel adaptive filtering | Kernel selection | CROSS-VALIDATION | STEADY-STATE | ALGORITHMS | SELECTION | ONLINE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Artificial intelligence | Robotics | Algorithms | Kernels | Reproduction | Least mean squares | Adaptive filters | Least mean squares algorithm | Hilbert space | Optimization

Kernel methods | Kernel least mean square | Kernel adaptive filtering | Kernel selection | CROSS-VALIDATION | STEADY-STATE | ALGORITHMS | SELECTION | ONLINE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Artificial intelligence | Robotics | Algorithms | Kernels | Reproduction | Least mean squares | Adaptive filters | Least mean squares algorithm | Hilbert space | Optimization

Journal Article

19.