13.3.12.2 Maximum Likelihood Estimation, Classification

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Maximum Likelihood. MLE.

Haslett, J.[John],
Maximum likelihood discriminant analysis on the plane using a Markovian model of spatial context,
PR(18), No. 3-4, 1985, pp. 287-296.
Elsevier DOI 0309
BibRef
Earlier: Abstract of future paper: PR(17), No. 6, 1984, pp. Page 677.
Elsevier DOI 0309
BibRef

Venkateswarlu, N.B., Raju, P.S.V.S.K.,
Three stage ML classifier,
PR(24), No. 11, 1991, pp. 1113-1116.
Elsevier DOI 0401
fast version of the maximum likelihood classifier. BibRef

Venkateswarlu, N.B., Balaji, S., Raju, P.S.V.S.K., Boyle, R.D.,
Some further results of three stage ML classification applied to remotely sensed images,
PR(27), No. 10, October 1994, pp. 1379-1396.
Elsevier DOI 0401
BibRef

Brillault-O'Mahony, B., Ellis, T.J.,
A Maximum Likelihood Approach to Feature Segmentation,
PR(26), No. 5, May 1993, pp. 787-798.
Elsevier DOI BibRef 9305

Zhang, J., Modestino, J.W., Langan, D.A.,
Maximum-Likelihood Parameter Estimation for Unsupervised Stochastic Model-Based Image Segmentation,
IP(3), No. 4, July 1994, pp. 404-420.
IEEE DOI
See also Cluster Validation for Unsupervised Stochastic Model-Based Image Segmentation. BibRef 9407

Fessler, J.A., Hero, III, A.O.,
Penalized maximum-likelihood image reconstruction using space-alternating generalized EM algorithms,
IP(4), No. 10, October 1995, pp. 1417-1429.
IEEE DOI 0402
BibRef

Li, T.F.[Tze Fen],
An efficient algorithm to find the MLE of prior probabilities of a mixture in pattern recognition,
PR(29), No. 2, February 1996, pp. 337-339.
Elsevier DOI 0401
maximum likelihood estimation. BibRef

Chen, C.H., Tu, T.M.,
Computation Reduction of the Maximum-Likelihood Classifier Using the Winograd Identity,
PR(29), No. 7, July 1996, pp. 1213-1220.
Elsevier DOI 9607
BibRef

McLachlan, G.J., Peel, D., Whiten, W.J.,
Maximum likelihood clustering via normal mixture models,
SP:IC(8), No. 2, March 1996, pp. 105-111.
Elsevier DOI
See also Bias associated with the discriminant analysis approach to the estimation of mixing proportions. BibRef 9603

McLachlan, G.J.[Geoff J.], Peel, D.,
Mixfit: An Algorithm for the Automatic Fitting and Testing of Normal Mixture Models,
ICPR98(Vol I: 553-557).
IEEE DOI 9808
BibRef

Zhou, Z.Y., Leahy, R.M., Qi, J.Y.,
Approximate Maximum-Likelihood Hyperparameter Estimation for Gibbs-Priors,
IP(6), No. 6, June 1997, pp. 844-861.
IEEE DOI 9705
BibRef

Zhou, Z.Y., Leahy, R.M.,
Approximate maximum likelihood hyperparameter estimation for Gibbs priors,
ICIP95(II: 284-287).
IEEE DOI 9510
BibRef

Handley, J.C., Dougherty, E.R.,
Maximum-Likelihood-Estimation for the Two-Dimensional Discrete Boolean Random Set and Function Models Using Multidimensional Linear Samples,
GMIP(59), No. 4, July 1997, pp. 221-231. 9709
BibRef

Handley, J.C.[John C.], Dougherty, E.R.[Edward R.],
Maximum-likelihood estimation and optimal filtering in the nondirectional, one-dimensional binomial germ-grain model,
PR(32), No. 9, September 1999, pp. 1529-1541.
Elsevier DOI BibRef 9909

Lee, C., Choi, E.,
Bayes Error Evaluation of the Gaussian ML Classifier,
GeoRS(38), No. 3, May 2000, pp. 1471-1475.
IEEE Top Reference. 0006
BibRef

Raudys, A.[Aistis], Long, J.A.,
MLP Based Linear Feature Extraction for Nonlinearly Separable Data,
PAA(4), No. 4 2001, pp. 227-234.
Springer DOI 0202
BibRef

Raudys, A.[Aistis],
Accuracy of MLP Based Data Visualization Used in Oil Prices Forecasting Task,
CIAP05(761-769).
Springer DOI 0509
BibRef

Hayat, M.M., Abdullah, M.S., Joobeur, A., Saleh, B.E.A.,
Maximum-likelihood image estimation using photon-correlated beams,
IP(11), No. 8, August 2002, pp. 838-846.
IEEE DOI 0209
BibRef

Hung, M.C.[Ming-Chih], Ridd, M.K.[Merrill K.],
A Subpixel Classifier for Urban Land-Cover Mapping Based on a Maximum-Likelihood Approach and Expert-System Rules,
PhEngRS(68), No. 11, November 2002, pp. 1173-1180. A supervised classifier based on a maximum-likelihood approach, TM image characteristics, the V-I-S model, and expert system rules, to estimate ground component composition of urban areas at the subpixel level.
WWW Link. 0304
BibRef

Xie, J., Tsui, H.T.,
Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED),
PRL(25), No. 10, 16 July 2004, pp. 1133-1141.
Elsevier DOI 0407
BibRef

Xie, J.[Jun], Tsui, H.T., Xia, D.S.[De-Shen],
Multiple objects segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED),
ICPR02(I: 707-710).
IEEE DOI 0211
BibRef

Meignen, S., Meignen, H.,
On the Modeling of Small Sample Distributions With Generalized Gaussian Density in a Maximum Likelihood Framework,
IP(15), No. 6, June 2006, pp. 1647-1652.
IEEE DOI 0606
Model distributions. BibRef

Pi, M.H.[Ming-Hong],
Improve maximum likelihood estimation for subband GGD parameters,
PRL(27), No. 14, 15 October 2006, pp. 1710-1713.
Elsevier DOI 0609
Generalized Gaussian density; Moment estimator; Maximum likelihood estimator; Newton-Raphson iteration; Regula-Falsi iteration BibRef

Zeng, G.L.[Gengsheng L.],
Filtered backprojection algorithm can outperform iterative maximum likelihood expectation-maximization algorithm,
IJIST(22), No. 2, June 2012, pp. 114-120.
DOI Link 1202
BibRef

Routtenberg, T., Tong, L.[Lang],
Joint Frequency and Phasor Estimation Under the KCL Constraint,
SPLetters(20), No. 6, 2013, pp. 575-578.
IEEE DOI 1307
least squares approximations; maximum likelihood estimation; BibRef

Guo, Q.[Qintian], Beaulieu, N.C.,
An Approximate ML Estimator for the Location Parameter of the Generalized Gaussian Distribution With p=5,
SPLetters(20), No. 7, 2013, pp. 677-680.
IEEE DOI 1307
maximum likelihood estimation BibRef

Zhang, H., Wei, P., Mou, Q.,
A Semidefinite Relaxation Approach to Blind Despreading of Long-Code DS-SS Signal With Carrier Frequency Offset,
SPLetters(20), No. 7, 2013, pp. 705-708.
IEEE DOI 1307
Convex functions; maximum likelihood estimate (MLE); semidefinite relaxation BibRef

Fang, W.H., Lee, Y.C., Chen, Y.T.,
Importance Sampling-Based Maximum Likelihood Estimation for Multidimensional Harmonic Retrieval,
SPLetters(23), No. 1, January 2016, pp. 35-39.
IEEE DOI 1601
Harmonic analysis BibRef

Babu, P.,
MELT: Maximum-Likelihood Estimation of Low-Rank Toeplitz Covariance Matrix,
SPLetters(23), No. 11, November 2016, pp. 1587-1591.
IEEE DOI 1609
Toeplitz matrices BibRef

Strelow, D.[Dennis], Wang, Q.F.[Qi-Fan], Si, L.[Luo], Eriksson, A.P.[Anders P.],
General, Nested, and Constrained Wiberg Minimization,
PAMI(38), No. 9, September 2016, pp. 1803-1815.
IEEE DOI 1609
Algorithm design and analysis BibRef

Strelow, D.[Dennis],
General and Nested Wiberg Minimization: L2 and Maximum Likelihood,
ECCV12(VII: 195-207).
Springer DOI 1210
BibRef
And:
General and nested Wiberg minimization,
CVPR12(1584-1591).
IEEE DOI 1208
BibRef

Selva, J.,
ML Estimation and Detection of Multiple Frequencies Through Periodogram Estimate Refinement,
SPLetters(24), No. 3, March 2017, pp. 249-253.
IEEE DOI 1702
Complexity theory BibRef

Lu, Q.[Qin], Bar-Shalom, Y.[Yaakov], Willett, P.[Peter], Zhou, S.L.[Sheng-Li],
Nonlinear Observation Models With Additive Gaussian Noises and Efficient MLEs,
SPLetters(24), No. 5, May 2017, pp. 545-549.
IEEE DOI 1704
Gaussian noise BibRef

Agarwal, R., Chen, Z., Sarma, S.V.,
A Novel Nonparametric Maximum Likelihood Estimator for Probability Density Functions,
PAMI(39), No. 7, July 2017, pp. 1294-1308.
IEEE DOI 1706
Computational modeling, Convergence, Kernel, Maximum likelihood estimation, Probability density function, Random variables, Maximum likelihood, density, estimation, neuronal receptive fields, nonparametric, pdf, tail, estimation BibRef

Ostrometzky, J., Messer, H.,
Comparison of Different Methodologies of Parameter-Estimation From Extreme Values,
SPLetters(24), No. 9, September 2017, pp. 1293-1297.
IEEE DOI 1708
exponential distribution, simulation, exponential distribution, extreme value theory, maximum likelihood estimation, parameter estimation, simulation, Complexity theory, Convergence, Gaussian distribution, Probability density function, Extreme value theory, BibRef

Li, P.H.[Pei-Hua], Wang, Q.L.[Qi-Long], Zeng, H., Zhang, L.,
Local Log-Euclidean Multivariate Gaussian Descriptor and Its Application to Image Classification,
PAMI(39), No. 4, April 2017, pp. 803-817.
IEEE DOI 1703
BibRef
Earlier: A1, A2, Only:
Local Log-Euclidean Covariance Matrix (L2ECM) for Image Representation and Its Applications,
ECCV12(III: 469-482).
Springer DOI 1210
Covariance matrices. either sparse interest points or dense image representations. BibRef

Wang, Q.L.[Qi-Long], Li, P.H.[Pei-Hua], Zhang, L.[Lei],
G2DeNet: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition,
CVPR17(6507-6516)
IEEE DOI 1711
Backpropagation, Covariance matrices, Gaussian distribution, Image representation, Manifolds, Matrix decomposition, Symmetric, matrices BibRef

Wang, Q.L.[Qi-Long], Li, P.H.[Pei-Hua], Zuo, W.M.[Wang-Meng], Zhang, L.[Lei],
RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian with Application to Material Recognition,
CVPR16(4433-4441)
IEEE DOI 1612
BibRef

Zozor, S., Ren, C., Renaux, A.,
On the Maximum Likelihood Estimator Statistics for Unimodal Elliptical Distributions in the High Signal-to-Noise Ratio Regime,
SPLetters(25), No. 6, June 2018, pp. 883-887.
IEEE DOI 1806
AWGN, Gaussian noise, maximum likelihood estimation, signal processing, statistical distributions, maximum likelihood estimator (MLE) statistics BibRef

Feitosa, A.E., Nascimento, V.H., Lopes, C.G.,
Adaptive Detection in Distributed Networks Using Maximum Likelihood Detector,
SPLetters(25), No. 7, July 2018, pp. 974-978.
IEEE DOI 1807
distributed sensors, least mean squares methods, maximum likelihood detection, maximum likelihood estimation, maximum likelihood (ML) detector BibRef

Ince, E.A., Allahdad, M.K., Yu, R.,
A Tensor Approach to Model Order Selection of Multiple Sinusoids,
SPLetters(25), No. 7, July 2018, pp. 1104-1108.
IEEE DOI 1807
AWGN, covariance matrices, entropy, maximum likelihood estimation, signal classification, singular value decomposition, tensors, order estimation BibRef

Tronarp, F., Karvonen, T., Särkkä, S.,
Student's t-Filters for Noise Scale Estimation,
SPLetters(26), No. 2, February 2019, pp. 352-356.
IEEE DOI 1902
covariance matrices, filtering theory, Gaussian processes, iterative methods, maximum likelihood estimation, optimisation, noise covariance estimation BibRef

Psutka, J.V.[Josef V.], Psutka, J.[Josef],
Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space,
PR(91), 2019, pp. 25-33.
Elsevier DOI 1904
Maximum-likelihood estimate, Likelihood function, Gaussian model, Gaussian mixture model, Sample size, Heteroscedastic data. BibRef

Mao, L., Gao, Y., Yan, S., Xu, L.,
Persymmetric Subspace Detection in Structured Interference and Non-Homogeneous Disturbance,
SPLetters(26), No. 6, June 2019, pp. 928-932.
IEEE DOI 1906
Detectors, Interference, Covariance matrices, Adaptation models, Maximum likelihood estimation, Object detection, Training data, partially homogeneous environment BibRef

Le Blanc, J.W.[Joel W.], Thelen, B.J.[Brian J.], Hero, A.O.[Alfred O.],
Testing that a Local Optimum of the Likelihood is Globally Optimum Using Reparameterized Embeddings,
JMIV(62), No. 6-7, July 2020, pp. 858-871.
Springer DOI 2007
BibRef

Huynh, H.T.[Hieu Trung], Nguyen, L.[Linh],
Nonparametric maximum likelihood estimation using neural networks,
PRL(138), 2020, pp. 580-586.
Elsevier DOI 2010
Neural network, Maximum likelihood estimation, Nonparametric, Probability density function BibRef

Vidal, A.F.[Ana Fernandez], de Bortoli, V.[Valentin], Pereyra, M.[Marcelo], Durmus, A.[Alain],
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments,
SIIMS(13), No. 4, 2020, pp. 1945-1989.
DOI Link 2012
BibRef

de Bortoli, V.[Valentin], Durmus, A.[Alain], Pereyra, M.[Marcelo], Vidal, A.F.[Ana Fernandez],
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis,
SIIMS(13), No. 4, 2020, pp. 1990-2028.
DOI Link 2012
BibRef

Manss, C., Shutin, D., Leus, G.,
Consensus Based Distributed Sparse Bayesian Learning by Fast Marginal Likelihood Maximization,
SPLetters(27), 2020, pp. 2119-2123.
IEEE DOI 2012
Signal processing algorithms, Optimization, Bayes methods, Estimation, Robot sensing systems, Convergence, Convex functions, sparse bayesian learning BibRef

Naumer, H., Kamalabadi, F.,
Estimation of Linear Space-Invariant Dynamics,
SPLetters(27), 2020, pp. 2154-2158.
IEEE DOI 2012
Maximum likelihood estimation, Mathematical model, Heuristic algorithms, Steady-state, Large scale integration, dynamical systems BibRef

Efendi, E., Dulek, B.,
Online EM-Based Ensemble Classification With Correlated Agents,
SPLetters(28), 2021, pp. 294-298.
IEEE DOI 2102
Signal processing algorithms, Parameter estimation, Maximum likelihood estimation, Correlation, Indexes, expectation-maximization BibRef

Lesouple, J., Pilastre, B., Altmann, Y., Tourneret, J.Y.,
Hypersphere Fitting From Noisy Data Using an EM Algorithm,
SPLetters(28), 2021, pp. 314-318.
IEEE DOI 2102
Signal processing algorithms, Maximum likelihood estimation, Noise measurement, Fitting, von Mises-Fisher distribution BibRef

Li, Y.P.[Yun-Peng], Ye, Z.H.[Zhao-Hui],
Boosting in Univariate Nonparametric Maximum Likelihood Estimation,
SPLetters(28), 2021, pp. 623-627.
IEEE DOI 2104
Boosting, Kernel, Splines (mathematics), Smoothing methods, Mathematical model, Signal processing algorithms, smoothing spline BibRef

Zhao, Y.[Yan], Wong, W.[Wai], Zheng, J.F.[Jian-Feng], Liu, H.X.[Henry X.],
Maximum Likelihood Estimation of Probe Vehicle Penetration Rates and Queue Length Distributions From Probe Vehicle Data,
ITS(23), No. 7, July 2022, pp. 7628-7636.
IEEE DOI 2207
Probes, Maximum likelihood estimation, Detectors, Queueing analysis, Trajectory, Real-time systems, Transportation, maximum likelihood estimation BibRef

Llosa-Vite, C.[Carlos], Maitra, R.[Ranjan],
Reduced-Rank Tensor-on-Tensor Regression and Tensor-Variate Analysis of Variance,
PAMI(45), No. 2, February 2023, pp. 2282-2296.
IEEE DOI 2301
Tensors, Analysis of variance, Faces, Maximum likelihood estimation, Linear regression, tucker format BibRef

Tucker, D.[David], Zhao, S.[Shen], Potter, L.C.[Lee C],
Maximum Likelihood Estimation in Mixed Integer Linear Models,
SPLetters(30), 2023, pp. 1557-1561.
IEEE DOI 2311
BibRef

Teimouri, M.[Mahdi],
A Fast and Simple Algorithm for Computing MLE of the Amplitude Density Function Parameters,
SPLetters(31), 2024, pp. 626-630.
IEEE DOI 2402
Maximum likelihood estimation, Synthetic aperture radar, Probability density function, GSM, Clutter, Tail, synthetic aperture radar (SAR) BibRef


Kan, G.[Ge], Lü, J.[Jinhu], Wang, T.[Tian], Zhang, B.C.[Bao-Chang], Zhu, A.[Aichun], Huang, L.[Lei], Guo, G.D.[Guo-Dong], Snoussi, H.[Hichem],
Bi-level Doubly Variational Learning for Energy-based Latent Variable Models,
CVPR22(18439-18448)
IEEE DOI 2210
Training, Visualization, Maximum likelihood estimation, Maximum likelihood detection, Stacking, Lead, Pattern recognition, Image and video synthesis and generation BibRef

Ye, F.[Fei], Bors, A.G.[Adrian G.],
InfoVAEGAN: Learning Joint Interpretable Representations by Information Maximization and Maximum Likelihood,
ICIP21(749-753)
IEEE DOI 2201
Training, Manifolds, Inference mechanisms, Tools, Generative adversarial networks, Generators, Mutual information BibRef

Ali, M.[Muhammad], Gao, J.B.[Jun-Bin], Antolovich, M.[Michael],
MLE-Based Learning on Grassmann Manifolds,
DICTA16(1-7)
IEEE DOI 1701
Computer vision BibRef

Harba, R.[Rachid], Douzi, H.[Hassan], El Hajji, M.[Mohamed],
Maximum Likelihood Estimation, Interpolation and Prediction for Fractional Brownian Motion,
ICISP12(326-332).
Springer DOI 1208
BibRef

Pletscher, P.[Patrick], Nowozin, S.[Sebastian], Kohli, P.[Pushmeet], Rother, C.[Carsten],
Putting MAP Back on the Map,
DAGM11(111-121).
Springer DOI 1109
Learning Conditional Random Fields (CRFs) models. BibRef

Okatani, T.[Takayuki], Deguchi, K.[Koichiro],
Improving accuracy of geometric parameter estimation using projected score method,
ICCV09(1733-1740).
IEEE DOI 0909
BibRef
And:
On bias correction for geometric parameter estimation in computer vision,
CVPR09(959-966).
IEEE DOI 0906
Bias in maximum likelihood estimation techniques due to geometric configurations. BibRef

Rastgar, H.[Houman], Zhang, L.[Liang], Wang, D.[Demin], Dubois, E.[Eric],
Validation of correspondences in MLESAC robust estimation,
ICPR08(1-4).
IEEE DOI 0812
maximum likelihood estimation sample consensus. BibRef

Nestares, O., Fleet, D.J.,
Error-in-variables likelihood functions for motion estimation,
ICIP03(III: 77-80).
IEEE DOI 0312
BibRef

Nestares, O.[Oscar], Fleet, D.J.[David J.], Heeger, D.J.[David J.],
Likelihood Functions and Confidence Bounds for Total-Least-Squares Problems,
CVPR00(I: 523-530).
IEEE DOI 0005
BibRef

Um, I.T., Ra, J.H., Kim, M.H.,
Comparison of Clustering Methods for MLP-based Speaker Verification,
ICPR00(Vol II: 475-478).
IEEE DOI 0009
BibRef

El Malek, J., Alimi, A.M., Tourki, R.,
Effect of the Feature Vector Size on the Generalization Error: The Case of MLPNN and RBFNN Classifiers,
ICPR00(Vol II: 630-633).
IEEE DOI 0009
BibRef

Gimel'farb, G.L.[Georgy L.],
On the Maximum Likelihood Potential Estimates for Gibbs Random Field Image Models,
ICPR98(Vol II: 1598-1600).
IEEE DOI 9808
BibRef

Grim, J.,
Maximum-Likelihood Design of Layered Neural Networks,
ICPR96(IV: 85-89).
IEEE DOI 9608
(Academy of Sciences, CZ) BibRef

Berrim, S., Lansiart, A., Moretti, J.L.,
Implementing of maximum likelihood in tomographical coded aperture,
ICIP96(II: 745-748).
IEEE DOI 9610
BibRef

Sun, Y.[Yi],
Tracking and detection of moving point targets in noise image sequences by local maximum likelihood,
ICIP96(III: 799-802).
IEEE DOI 9610
BibRef

Moghaddam, B., Pentland, A.,
A subspace method for maximum likelihood target detection,
ICIP95(III: 512-515).
IEEE DOI 9510
BibRef

Meir, R.,
Empirical risk minimization versus maximum-likelihood estimation: A case study,
ICPR94(B:295-299).
IEEE DOI 9410
BibRef

Endoh, T., Toriu, T., Tagawa, N.,
The maximum likelihood estimator is not 'optimal' on 3-D motion estimation from noisy optical flow,
ICIP94(II: 247-251).
IEEE DOI 9411
BibRef

Tagawa, N., Toriu, T., Endoh, T.,
An objective function for 3-D motion estimation from optical flow with lower error variance than maximum likelihood estimator,
ICIP94(II: 252-256).
IEEE DOI 9411
BibRef

Schultz, R.R., Stevenson, R.L., Lumsdaine, A.,
Maximum likelihood parameter estimation for non-Gaussian prior signal models,
ICIP94(II: 700-704).
IEEE DOI 9411
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Energy Minimization, Energy Maximization Computation, Function Solving .


Last update:Mar 16, 2024 at 20:36:19