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0209
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Oscillating Search Algorithms for Feature Selection,
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IEEE DOI
0009
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0611
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0609
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0406
Predict criterion values to improve search.
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0608
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0304
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0409
Learn both optimal classifier and the subset of relevant features.
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0306
Recast branch-and-bound feature selection as linear programming.
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0301
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Prototype reduction schemes (PRS)
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Kim, S.W.[Sang-Woon],
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0412
PCA.
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0501
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Kim, S.W.[Sang-Woon],
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0512
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Kim, S.W.[Sang-Woon],
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0707
BibRef
Earlier:
On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype
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SSPR06(826-834).
Springer DOI
0608
BibRef
And:
On Optimizing Dissimilarity-Based Classification Using Prototype
Reduction Schemes,
ICIAR06(I: 15-28).
Springer DOI
0610
Dissimilarity representation, Dissimilarity-based classification,
Prototype reduction schemes (PRSs), Mahalanobis distances (MDs)
See also On Optimizing Subclass Discriminant Analysis Using a Pre-clustering Technique.
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Tahir, M.A.[Muhammad Atif],
Smith, J.[Jim],
Creating diverse nearest-neighbour ensembles using simultaneous
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PRL(31), No. 11, 1 August 2010, pp. 1470-1480.
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1008
Tabu Search, 1NN classifier, Feature selection, Ensemble classifiers
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Kim, S.W.[Sang-Woon],
An empirical evaluation on dimensionality reduction schemes for
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PRL(32), No. 6, 15 April 2011, pp. 816-823.
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1103
Dissimilarity-based classifications, Dimensionality reduction schemes;
Prototype selection methods, Linear discriminant analysis
BibRef
Kim, S.W.[Sang-Woon],
Oommen, B.J.[B. John],
On using prototype reduction schemes to enhance the computation of
volume-based inter-class overlap measures,
PR(42), No. 11, November 2009, pp. 2695-2704.
Elsevier DOI
0907
Prototype reduction schemes (PRS), k-nearest neighbor (k-NN)
classifier, Data complexity, Class-overlapping
BibRef
Kim, S.W.[Sang-Woon],
Gao, J.[Jian],
A Dynamic Programming Technique for Optimizing Dissimilarity-Based
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SSPR08(654-663).
Springer DOI
0812
BibRef
And:
On Using Dimensionality Reduction Schemes to Optimize
Dissimilarity-Based Classifiers,
CIARP08(309-316).
Springer DOI
0809
BibRef
Oh, I.S.[Il-Seok],
Lee, J.S.[Jin-Seon],
Moon, B.R.[Byung-Ro],
Hybrid Genetic Algorithms for Feature Selection,
PAMI(26), No. 11, November 2004, pp. 1424-1437.
IEEE Abstract.
0410
BibRef
Earlier:
Local search-embedded genetic algorithms for feature selection,
ICPR02(II: 148-151).
IEEE DOI
0211
BibRef
Krishnapuram, B.[Balaji],
Carin, L.[Lawrence],
Figueiredo, M.A.T.[Mario A.T.],
Hartemink, A.J.[Alexander J.],
Sparse Multinomial Logistic Regression:
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PAMI(27), No. 6, June 2005, pp. 957-968.
IEEE Abstract.
0505
Sparse learning. Multiclass formulation based on regression,
combine using optimization and a component update procedure.
BibRef
Liu, Y.[Yi],
Zheng, Y.F.[Yuan F.],
FS_SFS: A novel feature selection method for support vector machines,
PR(39), No. 7, July 2006, pp. 1333-1345.
Elsevier DOI
0606
Sequential forward search, Support vector machines
BibRef
Wang, X.Y.[Xiang-Yang],
Yang, J.[Jie],
Teng, X.L.[Xiao-Long],
Xia, W.J.[Wei-Jun],
Jensen, R.[Richard],
Feature selection based on rough sets and particle swarm optimization,
PRL(28), No. 4, 1 March 2007, pp. 459-471.
Elsevier DOI
0701
Feature selection, Rough sets, Reduct, Genetic algorithms;
Particle swarm optimization, Hill-climbing method, Stochastic method
BibRef
Zhang, P.[Ping],
Verma, B.[Brijesh],
Kumar, K.[Kuldeep],
Neural vs. statistical classifier in conjunction with genetic algorithm
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PRL(26), No. 7, 15 May 2005, pp. 909-919.
Elsevier DOI
0506
BibRef
Hong, J.H.[Jin-Hyuk],
Cho, S.B.[Sung-Bae],
Efficient huge-scale feature selection with speciated genetic algorithm,
PRL(27), No. 2, 15 January 2006, pp. 143-150.
Elsevier DOI
0512
BibRef
Huang, J.J.[Jin-Jie],
Cai, Y.Z.[Yun-Ze],
Xu, X.M.[Xiao-Ming],
A hybrid genetic algorithm for feature selection wrapper based on
mutual information,
PRL(28), No. 13, 1 October 2007, pp. 1825-1844.
Elsevier DOI
0709
BibRef
Earlier:
A Wrapper for Feature Selection Based on Mutual Information,
ICPR06(II: 618-621).
IEEE DOI
0609
Machine learning, Hybrid genetic algorithm, Feature selection,
Mutual information
BibRef
Nakariyakul, S.[Songyot],
Casasent, D.P.[David P.],
Adaptive branch and bound algorithm for selecting optimal features,
PRL(28), No. 12, 1 September 2007, pp. 1415-1427.
Elsevier DOI
0707
Branch and bound algorithm, Dimensionality reduction, Feature selection;
Optimal subset search
BibRef
Gavrilis, D.[Dimitris],
Tsoulos, I.G.[Ioannis G.],
Dermatas, E.[Evangelos],
Selecting and constructing features using grammatical evolution,
PRL(29), No. 9, 1 July 2008, pp. 1358-1365.
Elsevier DOI
0711
Keywords: Artificial neural networks, Feature selection,
Feature construction, Genetic programming, Grammatical evolution
BibRef
Nakariyakul, S.[Songyot],
Casasent, D.P.[David P.],
An improvement on floating search algorithms for feature subset
selection,
PR(42), No. 9, September 2009, pp. 1932-1940.
Elsevier DOI
0905
Dimensionality reduction, Feature selection, Floating search methods;
Weak feature replacement
BibRef
Nakariyakul, S.[Songyot],
Suboptimal branch and bound algorithms for feature subset selection:
A comparative study,
PRL(45), No. 1, 2014, pp. 62-70.
Elsevier DOI
1407
BibRef
Earlier:
A new feature selection algorithm for multispectral and polarimetric
vehicle images,
ICIP09(2865-2868).
IEEE DOI
0911
Branch and bound algorithm
BibRef
Hong, Y.[Yi],
Kwong, S.[Sam],
To combine steady-state genetic algorithm and ensemble learning for
data clustering,
PRL(29), No. 9, 1 July 2008, pp. 1416-1423.
Elsevier DOI
0711
Clustering analysis, Ensemble learning, Genetic-guided clustering algorithms
BibRef
Hong, Y.[Yi],
Kwong, S.[Sam],
Wang, H.[Hanli],
Ren, Q.S.[Qing-Sheng],
Resampling-based selective clustering ensembles,
PRL(30), No. 3, 1 February 2009, pp. 298-305.
Elsevier DOI
0804
Clustering analysis, Clustering ensembles, Resampling technique
BibRef
Yusta, S.C.[Silvia Casado],
Different metaheuristic strategies to solve the feature selection
problem,
PRL(30), No. 5, 1 April 2009, pp. 525-534.
Elsevier DOI
0903
Feature selection, Floating search, Genetic Algorithm, GRASP, Tabu
Search, Memetic Algorithm
BibRef
Wang, Y.[Yong],
Li, L.[Lin],
Ni, J.[Jun],
Huang, S.H.[Shu-Hong],
Feature selection using tabu search with long-term memories and
probabilistic neural networks,
PRL(30), No. 7, 1 May 2009, pp. 661-670.
Elsevier DOI
0904
Feature selection, Tabu Search, Probabilistic neural network,
Smoothing parameter
BibRef
Park, M.S.[Myoung Soo],
Choi, J.Y.[Jin Young],
Theoretical analysis on feature extraction capability of
class-augmented PCA,
PR(42), No. 11, November 2009, pp. 2353-2362.
Elsevier DOI
0907
Feature extraction, CA-PCA (class-augmented principal component
analysis), Class information, PCA (principal component analysis);
Classification
BibRef
Sun, Y.J.[Yi-Jun],
Todorovic, S.[Sinisa],
Goodison, S.[Steve],
Local-Learning-Based Feature Selection for High-Dimensional Data
Analysis,
PAMI(32), No. 9, September 2010, pp. 1610-1626.
IEEE DOI
1008
BibRef
Cebe, M.[Mumin],
Gunduz-Demir, C.[Cigdem],
Qualitative test-cost sensitive classification,
PRL(31), No. 13, 1 October 2010, pp. 2043-2051.
Elsevier DOI
1003
Cost-sensitive learning, Qualitative decision theory, Feature
extraction cost, Feature selection
BibRef
Rodriguez-Lujan, I.,
Cruz, C.S.[C. Santa],
Huerta, R.,
On the equivalence of Kernel Fisher discriminant analysis and Kernel
Quadratic Programming Feature Selection,
PRL(32), No. 11, 1 August 2011, pp. 1567-1571.
Elsevier DOI
1108
Kernel Fisher discriminant, Quadratic Programming Feature Selection;
Feature selection, Kernel methods
BibRef
Shah, M.[Mohak],
Marchand, M.[Mario],
Corbeil, J.[Jacques],
Feature Selection with Conjunctions of Decision Stumps and Learning
from Microarray Data,
PAMI(34), No. 1, January 2012, pp. 174-186.
IEEE DOI
1112
Finding features that are consistent and reliable.
BibRef
Liu, J.[Jing],
Zhao, F.[Feng],
Liu, Y.[Yi],
Learning kernel parameters for kernel Fisher discriminant analysis,
PRL(34), No. 9, July 2013, pp. 1026-1031.
Elsevier DOI
1305
Kernel Fisher discriminant analysis (KFDA), Kernel parameter
optimization, Feature extraction, Spectral regression kernel
discriminant analysis (SRKDA)
BibRef
Liu, B.[Bo],
Fang, B.[Bin],
Liu, X.W.[Xin-Wang],
Chen, J.[Jie],
Huang, Z.H.[Zheng-Hong],
He, X.P.[Xi-Ping],
Large Margin Subspace Learning for feature selection,
PR(46), No. 10, October 2013, pp. 2798-2806.
Elsevier DOI
1306
Feature selection, l 2 , 1 - norm regularization, Large
margin maximization, Subspace learning
BibRef
Shu, W.H.[Wen-Hao],
Shen, H.[Hong],
Incremental feature selection based on rough set in dynamic
incomplete data,
PR(47), No. 12, 2014, pp. 3890-3906.
Elsevier DOI
1410
Feature selection
BibRef
Shu, W.H.[Wen-Hao],
Shen, H.[Hong],
Multi-criteria feature selection on cost-sensitive data with missing
values,
PR(51), No. 1, 2016, pp. 268-280.
Elsevier DOI
1601
Feature selection
BibRef
Naghibi, T.,
Hoffmann, S.,
Pfister, B.,
A Semidefinite Programming Based Search Strategy for Feature
Selection with Mutual Information Measure,
PAMI(37), No. 8, August 2015, pp. 1529-1541.
IEEE DOI
1507
Approximation algorithms
BibRef
Ben Brahim, A.[Afef],
Limam, M.[Mohamed],
A hybrid feature selection method based on instance learning and
cooperative subset search,
PRL(69), No. 1, 2016, pp. 28-34.
Elsevier DOI
1601
Feature selection
BibRef
Huang, D.,
Cabral, R.S.,
de la Torre, F.,
Robust Regression,
PAMI(38), No. 2, February 2016, pp. 363-375.
IEEE DOI
1601
Computational modeling
BibRef
Wang, W.,
Yan, Y.,
Winkler, S.,
Sebe, N.,
Category Specific Dictionary Learning for Attribute Specific Feature
Selection,
IP(25), No. 3, March 2016, pp. 1465-1478.
IEEE DOI
1602
Dictionaries
BibRef
Wang, W.[Wei],
Yan, Y.[Yan],
Nie, F.P.[Fei-Ping],
Yan, S.C.[Shui-Cheng],
Sebe, N.[Nicu],
Flexible Manifold Learning With Optimal Graph for Image and Video
Representation,
IP(27), No. 6, June 2018, pp. 2664-2675.
IEEE DOI
1804
eigenvalues and eigenfunctions, graph theory,
image classification, image representation, iterative methods,
graph embedding
BibRef
Wang, W.[Wei],
Yan, Y.[Yan],
Nie, F.P.[Fei-Ping],
Pineda, X.[Xavier],
Yan, S.C.[Shui-Cheng],
Sebe, N.[Nicu],
Projective Unsupervised Flexible Embedding with Optimal Graph,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Mohsenzadeh, Y.[Yalda],
Sheikhzadeh, H.[Hamid],
Nazari, S.[Sobhan],
Incremental relevance sample-feature machine:
A fast marginal likelihood maximization approach for
joint feature selection and classification,
PR(60), No. 1, 2016, pp. 835-848.
Elsevier DOI
1609
Sparse Bayesian learning
BibRef
Wang, X.D.[Xiao-Dong],
Chen, R.C.[Rung-Ching],
Yan, F.[Fei],
Zeng, Z.Q.[Zhi-Qiang],
Semi-supervised feature selection with exploiting shared information
among multiple tasks,
JVCIR(41), No. 1, 2016, pp. 272-280.
Elsevier DOI
1612
Semi-supervised learning
BibRef
Wang, X.D.[Xiao-Dong],
Chen, R.C.[Rung-Ching],
Hong, C.Q.[Chao-Qun],
Zeng, Z.Q.[Zhi-Qiang],
Unsupervised feature analysis with sparse adaptive learning,
PRL(102), 2018, pp. 89-94.
Elsevier DOI
1802
Unsupervised learning, Feature selection,
Adaptive structure learning, -Norm
BibRef
Zeng, Z.Q.[Zhi-Qiang],
Wang, X.D.[Xiao-Dong],
Chen, Y.M.[Yu-Ming],
Multimedia annotation via semi-supervised shared-subspace feature
selection,
JVCIR(48), No. 1, 2017, pp. 386-395.
Elsevier DOI
1708
Semi-supervised, learning
BibRef
Barbu, A.[Adrian],
She, Y.Y.[Yi-Yuan],
Ding, L.J.[Liang-Jing],
Gramajo, G.[Gary],
Feature Selection with Annealing for Computer Vision and Big Data
Learning,
PAMI(39), No. 2, February 2017, pp. 272-286.
IEEE DOI
1702
Algorithm design and analysis
BibRef
Zhou, H.J.[Hong-Jun],
You, M.Y.[Ming-Yu],
Liu, L.[Lei],
Zhuang, C.[Chao],
Sequential data feature selection for human motion recognition via
Markov blanket,
PRL(86), No. 1, 2017, pp. 18-25.
Elsevier DOI
1702
Sequential data
BibRef
Piza-Davila, I.[Ivan],
Sanchez-Diaz, G.[Guillermo],
Lazo-Cortes, M.S.[Manuel S.],
Rizo-Dominguez, L.[Luis],
A CUDA-based hill-climbing algorithm to find irreducible testors from
a training matrix,
PRL(95), No. 1, 2017, pp. 22-28.
Elsevier DOI
1708
Pattern recognition
BibRef
Wang, K.Z.[Kun-Zhe],
Xiao, H.T.[Huai-Tie],
Sparse kernel feature extraction via support vector learning,
PRL(101), No. 1, 2018, pp. 67-73.
Elsevier DOI
1801
Kernel principal component analysis
BibRef
Zhao, Y.[Yue],
You, X.G.[Xin-Ge],
Yu, S.J.[Shu-Jian],
Xu, C.[Chang],
Yuan, W.[Wei],
Jing, X.Y.[Xiao-Yuan],
Zhang, T.P.[Tai-Ping],
Tao, D.C.[Da-Cheng],
Multi-view manifold learning with locality alignment,
PR(78), 2018, pp. 154-166.
Elsevier DOI
1804
discover the low dimensional space where the input high dimensional
data are embedded.
Manifold learning, Multi-view learning, Locality alignment
BibRef
Liu, J.H.[Jing-Hua],
Lin, Y.J.[Yao-Jin],
Li, Y.[Yuwen],
Weng, W.[Wei],
Wu, S.X.[Shun-Xiang],
Online multi-label streaming feature selection based on neighborhood
rough set,
PR(84), 2018, pp. 273-287.
Elsevier DOI
1809
Online feature selection, Multi-label learning,
Neighborhood rough set, Granularity
BibRef
Peng, Y.[Yali],
Sehdev, P.[Paramjit],
Liu, S.G.[Shi-Gang],
Li, J.[Jun],
Wang, X.L.[Xi-Li],
L_2,1-norm minimization based negative label relaxation linear
regression for feature selection,
PRL(116), 2018, pp. 170-178.
Elsevier DOI
1812
BibRef
Yang, X.L.[Xiang-Lin],
Wang, Y.J.[Yu-Jing],
Ou, Y.[Yang],
Tong, Y.H.[Yun-Hai],
Three-Fast-Inter Incremental Association Markov Blanket learning
algorithm,
PRL(122), 2019, pp. 73-78.
Elsevier DOI
1904
Markov blanket, IAMB, Bayesian network
BibRef
Li, C.S.[Chang-Sheng],
Wang, X.F.[Xiang-Feng],
Dong, W.S.[Wei-Shan],
Yan, J.C.[Jun-Chi],
Liu, Q.S.[Qing-Shan],
Zha, H.Y.[Hong-Yuan],
Joint Active Learning with Feature Selection via CUR Matrix
Decomposition,
PAMI(41), No. 6, June 2019, pp. 1382-1396.
IEEE DOI
1905
Feature selection.
Feature extraction, Matrix decomposition,
Image reconstruction, Iterative methods, Labeling, Optimization,
matrix factorization
BibRef
Shang, R.H.[Rong-Hua],
Meng, Y.[Yang],
Wang, W.B.[Wen-Bing],
Shang, F.H.[Fan-Hua],
Jiao, L.C.[Li-Cheng],
Local discriminative based sparse subspace learning for feature
selection,
PR(92), 2019, pp. 219-230.
Elsevier DOI
1905
Local discriminant model, Subspace learning,
Sparse constraint, Feature selection
BibRef
Shah, M.H.,
Dang, X.,
Novel Feature Selection Method Using Bhattacharyya Distance for
Neural Networks Based Automatic Modulation Classification,
SPLetters(27), 2020, pp. 106-110.
IEEE DOI
2001
Modulation, Feature extraction, Training, Neural networks,
Probability distribution, Signal processing algorithms,
CNN
BibRef
Yu, K.[Kui],
Liu, L.[Lin],
Li, J.Y.[Jiu-Yong],
Ding, W.[Wei],
Le, T.D.[Thuc Duy],
Multi-Source Causal Feature Selection,
PAMI(42), No. 9, September 2020, pp. 2240-2256.
IEEE DOI
2008
Feature extraction, Diseases, Training, Search problems, Reliability,
Predictive models, Markov processes, Causal feature selection,
causal invariance
BibRef
Song, X.F.[Xian-Fang],
Zhang, Y.[Yong],
Gong, D.W.[Dun-Wei],
Sun, X.Y.[Xiao-Yan],
Feature selection using bare-bones particle swarm optimization with
mutual information,
PR(112), 2021, pp. 107804.
Elsevier DOI
2102
Feature selection, Particle swarm, Swarm initialization,
Mutual information, Local search
BibRef
Ma, W.P.[Wen-Ping],
Zhou, X.B.[Xiao-Bo],
Zhu, H.[Hao],
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Elsevier DOI
2402
Incremental feature selection, Tolerance rough set,
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Learning Discriminative Features with Class Encoder,
Robust16(1119-1125)
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Learning Multiple Complex Features Based on Classification Results,
ICPR14(3369-3373)
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1412
Accuracy
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Robust feature selection with self-matching score,
ICIP13(4363-4366)
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1402
compact visual descriptor;mobile visual search;self-matching score
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Optimizing Feature Selection through Binary Charged System Search,
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Semi-supervised learning with kernel locality-constrained linear coding,
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For low levels of labeled data, both labeled and unlabeled data.
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CIARP11(675-682).
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1111
For combinations of methods for supervised learning.
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Feature Selection with Complexity Measure in a Quadratic Programming
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1106
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1008
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1008
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0409
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