14.2.2.1 Binary Clustering, Two Class Classification

Chapter Contents (Back)
Clustering. Two Class. Binary Clustering.

Duchene, J.,
A Significant Plane for Two-Class Discrimination Problems,
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Aladjem, M.[Mayer],
Linear Discriminant-Analysis for Two Classes via Removal of Classification Structure,
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IEEE DOI 9703
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Two-Class Pattern Discrimination via Recursive Optimization of Patrick-Fisher Distance,
ICPR96(II: 60-64).
IEEE DOI 9608
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Earlier:
Discriminant plots obtained via removal of classification structures,
ICPR94(B:67-71).
IEEE DOI 9410
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Aladjem, M.[Mayer],
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Aladjem, M.[Mayer],
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Hand, D.J.[David J.], Vinciotti, V.[Veronica],
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He, C.[Chao], Girolami, M.A.[Mark A.],
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Reduced set density estimator. Binary classification. BibRef

DeVore, M.D.,
Estimates of Error Probability for Complex Gaussian Channels with Generalized Likelihood Ratio Detection,
PAMI(27), No. 10, October 2005, pp. 1580-1591.
IEEE DOI 0509
Two-class hypothesis testing. BibRef

Marrocco, C.[Claudio], Molinara, M.[Mario], Tortorella, F.[Francesco],
Exploiting AUC for optimal linear combinations of dichotomizers,
PRL(27), No. 8, June 2006, pp. 900-907.
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Earlier:
AUC-Based Linear Combination of Dichotomizers,
SSPR06(714-722).
Springer DOI 0608
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Earlier:
Estimating the ROC Curve of Linearly Combined Dichotomizers,
CIAP05(778-785).
Springer DOI 0509
Two-class classifiers; ROC curve; Multiple classifier systems; Linear combiners See also Towards a Linear Combination of Dichotomizers by Margin Maximization. BibRef

El Ayadi, M.M.H.[Moataz M.H.], Kamel, M.S.[Mohamed S.], Karray, F.[Fakhri],
Toward a tight upper bound for the error probability of the binary Gaussian classification problem,
PR(41), No. 6, June 2008, pp. 2120-2132.
Elsevier DOI 0802
Binary classification; Bayesian decision rule; Decision boundary; Error probability; Monte-Carlo simulations; Multivariate normal distribution; Quadratic surfaces BibRef

Bounsiar, A.[Abdenour], Beauseroy, P.[Pierre], Grall-Maes, E.[Edith],
General solution and learning method for binary classification with performance constraints,
PRL(29), No. 10, 15 July 2008, pp. 1455-1465.
Elsevier DOI 0711
Statistical hypothesis testing; Performance constraints; Neyman-Pearson criterion; Chow's rule; Classification with rejection option; Kernel methods BibRef

Chen, W., Metz, C.E., Giger, M.L., Drukker, K.,
A Novel Hybrid Linear/Nonlinear Classifier for Two-Class Classification: Theory, Algorithm, and Applications,
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Zhong, S.P.[Shang-Ping], Chen, D.[Daya], Xu, Q.F.[Qiao-Fen], Chen, T.S.[Tian-Shun],
Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification,
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Elsevier DOI 1303
Gaussian kernel function; Fast kernel learning method; Two-class pattern classification; Formulated kernel target alignment criterion; Euler-Maclaurin formula; Determined global minimum point; High time efficiency BibRef

Liu, M.X.[Ming-Xia], Zhang, D.Q.[Dao-Qiang], Chen, S.C.[Song-Can], Xue, H.[Hui],
Joint Binary Classifier Learning for ECOC-Based Multi-Class Classification,
PAMI(38), No. 11, November 2016, pp. 2335-2341.
IEEE DOI 1610
Classification algorithms BibRef

Yellamraju, T., Boutin, M.[Mireille],
Clusterability and Clustering of Images and Other 'Real' High-Dimensional Data,
IP(27), No. 4, April 2018, pp. 1927-1938.
IEEE DOI 1802
image representation, pattern clustering, probability, 1D random projection, binary clustering, random projection BibRef


Bauckhage, C.[Christian], Brito, E., Cvejoski, K., Ojeda, C., Sifa, R.[Rafet], Wrobel, S.,
Ising Models for Binary Clustering via Adiabatic Quantum Computing,
EMMCVPR17(3-17).
Springer DOI 1805
BibRef

Lin, W.J.,
Two-class clustering of nonlinearly separable data by using shape-specific points,
IVMSP16(1-5)
IEEE DOI 1608
Clustering algorithms BibRef

Ali, M.[Mohsen], Ho, J.[Jeffrey],
Deconstructing Binary Classifiers in Computer Vision,
ACCV14(III: 468-482).
Springer DOI 1504
BibRef

Zhang, X.[Xiao], Liang, L.[Lin], Shum, H.Y.[Heung-Yeung],
Spectral error correcting output codes for efficient multiclass recognition,
ICCV09(1111-1118).
IEEE DOI 0909
ECOC framework extends to any binary classifier in multi-class case. NP Hard problem. Get approximation. BibRef

Fainzilberg, L.S.,
Why Relevant Features May Be Unuseful in Statistical Recognition of Two Classes,
ICPR96(II: 730-734).
IEEE DOI 9608
BibRef
Earlier:
Interconnection between features properties and probability of error in statistical recognition of two classes,
ICPR94(B:544-546).
IEEE DOI 9410
(V. M. Glushkov Institute of Cybernetics, UKR) BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
One Class Clustering, One Class Classification .


Last update:Aug 16, 2018 at 18:22:30