Maximum Likelihood Estimation, Classification

Chapter Contents (Back)
Maximum Likelihood.

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.
WWW Version. 0309
Earlier: PR(17), No. 6, 1984, pp. Page 677.
WWW Version. 0309

Venkateswarlu, N.B., Raju, P.S.V.S.K.,
Three stage ML classifier,
PR(24), No. 11, 1991, pp. 1113-1116.
WWW Version. 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.
WWW Version. 0401

Brillault-O'Mahony, B., Ellis, T.J.,
A Maximum Likelihood Approach to Feature Segmentation,
PR(26), No. 5, May 1993, pp. 787-798.
WWW Version. 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 via 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 via DOI 0402

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.
WWW Version. 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.
WWW Version. 9607

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.
WWW Version. 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 via DOI 9808

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 via DOI 9705

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

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

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.
WWW Version. BibRef 9909

Vehtari, A.[Aki], Lampinen, J.[Jouko],
Bayesian MLP neural networks for image analysis,
PRL(21), No. 13-14, December 2000, pp. 1183-1191. 0011
Bayesian Neural Networks for Image Analysis,
SCIA99(Neural Nets). BibRef

Toivanen, M.[Miika], Lampinen, J.[Jouko],
Incremental Bayesian learning of feature points from natural images,
IEEE via DOI 0906

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

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

Raudys, A.[Aistis],
Accuracy of MLP Based Data Visualization Used in Oil Prices Forecasting Task,
Springer via DOI 0509

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 via DOI 0209

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 Version. 0304

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.
WWW Version. 0407

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 via DOI 0211

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 via 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.
WWW Version. 0609
Generalized Gaussian density; Moment estimator; Maximum likelihood estimator; Newton-Raphson iteration; Regula-Falsi iteration BibRef

Routtenberg, T., Tong, L.[Lang],
Joint Frequency and Phasor Estimation Under the KCL Constraint,
SPLetters(20), No. 6, 2013, pp. 575-578.
IEEE via 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 via 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 via DOI 1307
Convex functions; maximum likelihood estimate (MLE); semidefinite relaxation BibRef

Harba, R.[Rachid], Douzi, H.[Hassan], El Hajji, M.[Mohamed],
Maximum Likelihood Estimation, Interpolation and Prediction for Fractional Brownian Motion,
Springer via DOI 1208

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

Rastgar, H.[Houman], Zhang, L.[Liang], Wang, D.[Demin], Dubois, E.[Eric],
Validation of correspondences in MLESAC robust estimation,
IEEE via 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 via DOI 0312

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 via DOI 0005

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

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 via DOI 0009

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

Grim, J.,
Maximum-Likelihood Design of Layered Neural Networks,
ICPR96(IV: 85-89).
IEEE via 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 via DOI 9610

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

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

Meir, R.,
Empirical risk minimization versus maximum-likelihood estimation: A case study,
IEEE via DOI 9410

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 via DOI 9411

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 via DOI 9411

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

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

Last update:Apr 12, 2014 at 19:55:22