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Stochastic Complexity (with discussion),
RoyalStat(B-49), 1987, pp. 223-239.
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Rissanen, J.J.[Jorma J.],
A Universal Prior for Integers and Esitmation by Minimum Description Length,
AMS(11), 1983, pp. 416-431.
See also Universal Data Compression System, A.
BibRef
8300
Rissanen, J.J.[Jorma J.],
The Minimum Description Length (MDL) Principle and its Applications
to Pattern Classification,
ICPR98(Invited Talk).
9808
BibRef
Leonardis, A.[Ales],
Bischof, H.[Horst],
An Efficient MDL-Based Construction of RBF Networks,
NN(11), No. 5, July 1998, pp. 963-973.
See also Robust Recognition Using Eigenimages.
BibRef
9807
Bischof, H.[Horst],
Leonardis, A.[Ales],
MDL-Based Design of Vector Quantizers,
ICPR98(Vol I: 891-893).
IEEE DOI
9808
BibRef
Leonardis, A.,
Bischof, H.,
Complexity Optimization Of Adaptive Rbf Networks,
ICPR96(IV: 654-658).
IEEE DOI
9608
(Technical Univ. of Vienna, A)
BibRef
Bischof, H.[Horst],
Leonardis, A.[Ales],
Selb, A.[Alexander],
MDL Principle for Robust Vector Quantisation,
PAA(2), No. 1, 1999, pp. 59-72.
BibRef
9900
Selb, A.,
Bischof, H.,
Leonardis, A.,
Fuzzy C-means in an MDL-framework,
ICPR00(Vol II: 740-743).
IEEE DOI
0009
BibRef
Canning, J.,
A Minimum Description Length Model for Recognizing Objects with
Variable Appearances (The Vapor Model),
PAMI(16), No. 10, October 1994, pp. 1032-1036.
IEEE DOI
BibRef
9410
Davies, R.H.,
Twining, C.J.,
Cootes, T.F.,
Waterton, J.C.,
Taylor, C.J.,
A minimum description length approach to statistical shape modeling,
MedImg(21), No. 5, May 2002, pp. 525-537.
IEEE Top Reference.
0206
MDL
BibRef
von Gioi, R.G.[Rafael Grompone],
Paulino, I.R.[Ignacio Ramirez],
Randall, G.[Gregory],
The Whole and the Parts: The Minimum Description Length Principle and
the A-Contrario Framework,
SIIMS(15), No. 3, 2022, pp. 1282-1313.
DOI Link
2208
BibRef
Ramírez, I.[Ignacio],
Tepper, M.[Mariano],
Bi-clustering via MDL-Based Matrix Factorization,
CIARP13(I:230-237).
Springer DOI
1311
BibRef
Lai, Y.N.[Yu Ning],
Yuen, S.Y.[Shiu Yin],
Successive-least-squares error algorithm on minimum description length
neural networks for time series prediction,
ICPR04(IV: 609-612).
IEEE DOI
0409
BibRef
Ericsson, A.,
Astrom, K.,
Minimizing the description length using steepest descent,
BMVC03(xx-yy).
HTML Version.
0409
BibRef
Thodberg, H.H.,
Olafsdottir, H.,
Adding Curvature to Minimum Description Length Shape Models,
BMVC03(xx-yy).
HTML Version.
0409
MDL
BibRef
Thodberg, H.H.,
Minimum Description Length Shape and Appearance Models,
MedicalImaging03(51-62).
BibRef
0300
Kudo, M.,
Shimbo, M.,
Selection of Classifiers Based on the MDL Principle Using
the VC Dimension,
ICPR96(II: 886-890).
IEEE DOI
9608
(Hokkaido Univ., J)
BibRef
Murthy, C.A.,
Chatterjee, N.,
Shankar, B.U.[B. Uma],
Majumder, D.D.[D. Dutta],
IRS Image Segmentation: Minimum Distance Classifier Approach,
ICPR92(I:781-784).
IEEE DOI
BibRef
9200
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Convex Hull Algorithms and Convexity Analysis .