14.4.3 Decision Trees

Chapter Contents (Back)
Minimal Spanning Tree. Spanning Tree. Decision Tree. Tree Classifiers. 9805

See also Random Forests Classification.

de Souza, P.[Peter],
Some decision network designs for pattern classification,
PR(15), No. 3, 1982, pp. 193-200.
Elsevier DOI 0309
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Tamura, S.[Shinichi],
Clustering based on multiple paths,
PR(15), No. 6, 1982, pp. 477-483.
Elsevier DOI 0309
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Kurzynski, M.W.[Marek W.],
The optimal strategy of a tree classifier,
PR(16), No. 1, 1983, pp. 81-87.
Elsevier DOI 0309

See also On the multistage Bayes classifier.
See also On the Identity of Optimal Strategies for Multistage Classifiers. BibRef

Quinlan, J.R.,
Induction of Decision Trees,
MachLearn(1), No. 1, 1986, pp. 81-106. BibRef 8600

Li, X.B.[Xiao-Bo], Dubes, R.C.[Richard C.],
Tree classifier design with a permutation statistic,
PR(19), No. 3, 1986, pp. 229-235.
Elsevier DOI 0309
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Shlien, S.[Seymour],
Multiple binary decision tree classifiers,
PR(23), No. 7, 1990, pp. 757-763.
Elsevier DOI 0401
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Park, Y.T.[Young-Tae], Sklansky, J.[Jack],
Automated design of linear tree classifiers,
PR(23), No. 12, 1990, pp. 1393-1412.
Elsevier DOI 0401
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And:
Fast tree classifiers,
ICPR90(I: 684-686).
IEEE DOI 9006
BibRef
Earlier:
Automated design of piecewise-linear classifiers of multiple-class data,
ICPR88(II: 1068-1071).
IEEE DOI 8811
BibRef

Brown, D.E.[Donald E.], Corruble, V.[Vincent], Pittard, C.L.[Clarence Louis],
A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems,
PR(26), No. 6, June 1993, pp. 953-961.
Elsevier DOI 0401
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Brown, D.E., Pittard, C.L., Park, H.,
Classification Trees with Optimal Multivariate Decision Nodes,
PRL(17), No. 7, June 10 1996, pp. 699-703. 9607
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Gelfand, S.B., Ravishankar, C.S., and Delp, E.J.,
An Iterative Growing and Pruning Algorithm for Classification Tree Design,
PAMI(13), No. 2, February 1991, pp. 163-174.
IEEE DOI BibRef 9102

Guo, H., Gelfand, S.B.,
Classification trees with neural network feature extraction,
CVPR92(183-188).
IEEE DOI 0403
BibRef

Safavian, S.R.[S. Rasoul], and Landgrebe, D.A.[David A.],
A Survey of Decision Tree Classifier Methodology,
SMC(21), No. 3, May 1991, pp. 660-674.
PDF File. Survey, Decision Tree. BibRef 9105

Zhou, X.J.[Xiao Jia], and Dillon, T.S.[Tharam S.],
A Statistical-Heuristic Feature Selection Criterion for Decision Tree Induction,
PAMI(13), No. 8, August 1991, pp. 834-841.
IEEE DOI BibRef 9108

Draper, B.A., Brodley, C.E., Utgoff, P.E.,
Goal-Directed Classification Using Linear Machine Decision Trees,
PAMI(16), No. 9, September 1994, pp. 888-893.
IEEE DOI
PS File. BibRef 9409

Sethi, I.K.[Ishwar K.], Yoo, J.H.[Jae H.],
Design Of Multicategory Multifeature Split Decision Trees Using Perceptron Learning,
PR(27), No. 7, July 1994, pp. 939-947.
Elsevier DOI BibRef 9407

Sethi, I.K.[Ishwar K.], Yoo, J.H.[Jae H.],
Structure-Driven Induction of Decision Tree Classifiers Through Neural Learning,
PR(30), No. 11, November 1997, pp. 1893-1904.
Elsevier DOI 9801
BibRef

Lovell, B.C., Bradley, A.P.,
The Multiscale Classifier,
PAMI(18), No. 2, February 1996, pp. 124-137.
IEEE DOI Decision Tree. BibRef 9602

Chaudhuri, D., Chaudhuri, B.B., Murthy, C.A.,
A Data-driven Procedure for Density-Estimation with Some Applications,
PR(29), No. 10, October 1996, pp. 1719-1736.
Elsevier DOI Probability Density Estimation. Minimal Spanning Tree. BibRef 9610

Esposito, F., Malerba, D., Semeraro, G.,
A Comparative-Analysis of Methods for Pruning Decision Trees,
PAMI(19), No. 5, May 1997, pp. 476-491.
IEEE DOI 9705
See the comment paper also. BibRef

Kay, J.,
A Comparative-Analysis of Methods for Pruning Decision Trees: Comment,
PAMI(19), No. 5, May 1997, pp. 492-493.
IEEE DOI 9705
BibRef

Malerba, D.[Donato], Esposito, F.[Floriana], Ceci, M.[Michelangelo], Appice, A.[Annalisa],
Top-down induction of model trees with regression and splitting nodes,
PAMI(26), No. 5, May 2004, pp. 612-625.
IEEE Abstract. 0404
Model trees extend regression trees by having multiple regression models. BibRef

Friedl, M.A., Brodley, C.E.,
Decision Tree Classification of Land-Cover from Remotely-Sensed Data,
RSE(61), No. 3, September 1997, pp. 399-409. 9708
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Jun, B.H., Kim, C.S., Song, H.Y., Kim, J.,
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees,
PAMI(19), No. 12, December 1997, pp. 1371-1375.
IEEE DOI 9712
BibRef

Chowdhury, N., Murthy, C.A.,
Minimal Spanning Tree-Based Clustering Technique: Relationship with Bayes Classifier,
PR(30), No. 11, November 1997, pp. 1919-1929.
Elsevier DOI 9801
Minimal Spanning Tree. BibRef

Lam, C.P., West, G.A.W., Caelli, T.M.,
Validation of Machine Learning Techniques: Decision Trees and Finite Training Set,
JEI(7), No. 1, January 1998, pp. 94-103. 9807
BibRef

Ho, T.K.[Tin Kam],
The Random Subspace Method for Constructing Decision Forests,
PAMI(20), No. 8, August 1998, pp. 832-844.
IEEE DOI BibRef 9808
Earlier:
C4.5 Decision Forests,
ICPR98(Vol I: 545-549).
IEEE DOI 9808
BibRef

Ho, T.K.[Tin Kam],
Exploratory analysis of point proximity in subspaces,
ICPR02(II: 196-199).
IEEE DOI 0211
BibRef

Krishnan, R., Sivakumar, G., Bhattacharya, P.,
A Search Technique for Rule Extraction from Trained Neural Networks,
PRL(20), No. 3, March 1999, pp. 273-280. BibRef 9903

Krishnan, R., Sivakumar, G., Bhattacharya, P.,
Extracting Decision Trees from Trained Neural Networks,
PR(32), No. 12, December 1999, pp. 1999-2009.
Elsevier DOI Decision Tree. BibRef 9912

Suárez, A.[Alberto], Lutsko, J.F.[James F.],
Globally Optimal Fuzzy Decision Trees for Classification and Regression,
PAMI(21), No. 12, December 1999, pp. 1297-1311.
IEEE DOI 0001
BibRef

Ciampi, A., Diday, E., Lebbe, J., Périnel, E., Vignes, R.,
Growing a tree classifier with imprecise data,
PRL(21), No. 9, August 2000, pp. 787-803. 0008
BibRef

Breiman, L.,
Bagging Predictors,
MachLearn(24), 1996, pp. 123-140.
See also Random Forests. BibRef 9600

Yang, C.J.[Chang-Jiang], Weng, J.Y.[Ju-Yang],
Visual motion based behavior learning using hierarchical discriminant regression,
PRL(23), No. 8, June 2002, pp. 1031-1038.
Elsevier DOI 0204
BibRef

Sethi, I.K.[Ishwar K.], Chatterjee, B.,
Efficient decision tree design for discrete variable pattern recognition problems,
PR(9), No. 4, 1977, pp. 197-206.
Elsevier DOI 0309
BibRef

Rounds, E.M.,
A combined nonparametric approach to feature selection and binary decision tree design,
PR(12), No. 5, 1980, pp. 313-317.
Elsevier DOI 0309
BibRef

Murthy, K.R.K., Keerthi, S.S., Murty, M.N.,
Rule prepending and post-pruning approach to incremental learning of decision lists,
PR(34), No. 8, August 2001, pp. 1697-1699.
Elsevier DOI 0105
BibRef

Priebe, C.E.[Carey E.], Marchette, D.J.[David J.], Healy, Jr., D.M.[Dennis M.],
Integrated Sensing and Processing Decision Trees,
PAMI(26), No. 6, June 2004, pp. 699-708.
IEEE Abstract. 0404
Optimize misclassification rate. BibRef

Haskell, R.E.[Richard E.], Lee, C.[Charles], Hanna, D.M.[Darrin M.],
Geno-fuzzy classification trees,
PR(37), No. 8, August 2004, pp. 1653-1659.
Elsevier DOI 0407
BibRef

Päivinen, N.[Niina],
Clustering with a minimum spanning tree of scale-free-like structure,
PRL(26), No. 7, 15 May 2005, pp. 921-930.
Elsevier DOI 0506
graph theoretic clustering BibRef

Pedrycz, W., Sosnowski, Z.A.,
Genetically Optimized Fuzzy Decision Trees,
SMC-B(35), No. 3, June 2005, pp. 633-641.
IEEE DOI 0508

See also Fuzzy sets in pattern recognition: Methodology and methods.
See also consensus-driven fuzzy clustering, A. BibRef

Pedrycz, W., Sosnowski, Z.A.,
C-fuzzy decision trees,
SMC-C(35), No. 4, November 2005, pp. 498-511.
IEEE DOI 0512
BibRef

Rokach, L., Maimon, O.[Oded],
Top-down induction of decision trees classifiers: A survey,
SMC-C(35), No. 4, November 2005, pp. 476-487.
IEEE DOI 0512
Survey, Decision Tree. BibRef

Kennard, M.J., Harch, B.D., Pusey, B.J., and Arthington, A.H.,
Accurately defining the reference condition for summary biotic metrics,
Hydrobiologia(572), No. 1, November 2006 pp. 151-170.
Springer DOI Application of decision trees. BibRef 0611

Zambon, M.[Michael], Lawrence, R.L.[Rick L.], Bunn, A.[Andrew], Powell, S.[Scott],
Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis,
PhEngRS(72), No. 1, January 2006, pp. 25-31.
WWW Link. 0602
Alternative splitting rules for classification tree analysis had only minor effects on overall accuracy results of classified imagery, although, individual class accuracies varied widely. BibRef

Lawrence, R.L.[Rick L.], Wright, A.[Andrea],
Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis,
PhEngRS(67), No. 10, October 2001, pp. 1137-1142. Rule-based classifications incorporating spectral and ancillary data.
WWW Link. 0201
BibRef

van de Vlag, D.E., Stein, A.,
Incorporating Uncertainty via Hierarchical Classification Using Fuzzy Decision Trees,
GeoRS(45), No. 1, January 2007, pp. 237-245.
IEEE DOI 0701
BibRef

Banfield, R.E.[Robert E.], Hall, L.O.[Lawrence O.], Bowyer, K.W.[Kevin W.], Kegelmeyer, W.P.,
A Comparison of Decision Tree Ensemble Creation Techniques,
PAMI(29), No. 1, January 2007, pp. 173-180.
IEEE DOI 0701
Evaluate Bagging and 7 other randomized based approaches for combinations. Randomized C4.5 (
See also Random Forests. ) Random subspaces (
See also Random Subspace Method for Constructing Decision Forests, The. ) Random Forests (
See also Random Forests. ) AdaBoost M1W (
See also How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code. ) and Bagging BibRef

Pérez, J.M.[Jesús M.], Muguerza, J.[Javier], Arbelaitz, O.[Olatz], Gurrutxaga, I.[Ibai], Martín, J.I.[José I.],
Combining multiple class distribution modified subsamples in a single tree,
PRL(28), No. 4, 1 March 2007, pp. 414-422.
Elsevier DOI 0701
Class distribution; Decision tree; Sampling; Comprehensibility; C4.5 BibRef

Gurrutxaga, I.[Ibai], Albisua, I.[Inaki], Arbelaitz, O.[Olatz], Martin, J.I.[Jose I.], Muguerza, J.[Javier], Perez, J.M.[Jesus M.], Perona, I.[Inigo],
SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index,
PR(43), No. 10, October 2010, pp. 3364-3373.
Elsevier DOI 1007
Hierarchical clustering; Post-processing; Cluster validity index
See also extensive comparative study of cluster validity indices, An. BibRef

Bertrand, G.[Gilles],
On the dynamics,
IVC(25), No. 4, April 2007, pp. 447-454.
Elsevier DOI 0702
Mathematical morphology; Dynamics; Graph; Watershed; Minimum spanning tree; Component tree Necessary and sufficient conditions which indicate when a transformation preserves the dynamics of the regional maxima. BibRef

Li, Y.J.[Yu-Jian],
A clustering algorithm based on maximal theta-distant subtrees,
PR(40), No. 5, May 2007, pp. 1425-1431.
Elsevier DOI 0702
Maximal theta-distant subtree; Minimal spanning tree; Clustering algorithm; Threshold cutting; Number of clusters BibRef

Yildiz, O.T.[Olcay Taner], Dikmen, O.[Onur],
Parallel univariate decision trees,
PRL(28), No. 7, May 2007, pp. 825-832.
Elsevier DOI 0703
Decision trees; Parallel processing; Univariate decision trees; Linear discriminant trees BibRef

Liu, Y.H.[Ying-Ho], Lin, C.C.[Chin-Chin], Lin, W.H.[Wen-Hsiung], Chang, F.[Fu],
Accelerating feature-vector matching using multiple-tree and sub-vector methods,
PR(40), No. 9, September 2007, pp. 2392-2399.
Elsevier DOI 0705
Deterministic approach; Decision trees; Fast matching method; Feature-vector matching; Multiple trees; Statistical approach; Sub-vector matching BibRef

Altincay, H.[Hakan],
Decision trees using model ensemble-based nodes,
PR(40), No. 12, December 2007, pp. 3540-3551.
Elsevier DOI 0709
Decision trees; Ensemble-based decision nodes; Model selection; Omnivariate decision trees; Random subspace method BibRef

Jin, S.Y.[Shu-Yuan], Yeung, D.S.[Daniel So], Wang, X.Z.[Xi-Zhao],
Network intrusion detection in covariance feature space,
PR(40), No. 8, August 2007, pp. 2185-2197.
Elsevier DOI 0704
Covariance feature space; Threshold based detection; Decision tree; Network intrusion detection; Detection effectiveness BibRef

Balagani, K.S.[Kiran S.], Phoha, V.V.[Vir V.],
On the Relationship Between Dependence Tree Classification Error and Bayes Error Rate,
PAMI(29), No. 10, October 2007, pp. 1866-1868.
IEEE DOI 0710
Analyze the results of:
See also Comments on approximating discrete probability distributions with dependence trees. Derive a better description. BibRef

Pino-Mejias, R.[Rafael], Jimenez-Gamero, M.D.[Maria-Dolores], Cubiles-de-la-Vega, M.D.[Maria-Dolores], Pascual-Acosta, A.[Antonio],
Reduced bootstrap aggregating of learning algorithms,
PRL(29), No. 3, 1 February 2008, pp. 265-271.
Elsevier DOI 0801
Bagging; Reduced bootstrap; Decision trees; Multilayer perceptron BibRef

Watanachaturaporn, P.[Pakorn], Arora, M.K.[Manoj K.], Varshney, P.K.[Pramod K.],
Multisource Classification Using Support Vector Machines: An Empirical Comparison with Decision Tree and Neural Network Classifiers,
PhEngRS(74), No. 2, February 2008, pp. 239-246.
WWW Link. 0803
An SVM based multi-source classification shows a significant increase in the classification accuracy with incorporation of ancillary data over the classification performed solely on the basis of spectral data from remote sensing sensors. BibRef

Twala, B.E.T.H., Jones, M.C., Hand, D.J.,
Good methods for coping with missing data in decision trees,
PRL(29), No. 7, 1 May 2008, pp. 950-956.
Elsevier DOI 0804
C4.5; CART; EM algorithm; Fractional cases; Missingness as attribute; Multiple imputation BibRef

Kang, D.K.[Dae-Ki], Sohn, K.[Kiwook],
Learning decision trees with taxonomy of propositionalized attributes,
PR(42), No. 1, January 2009, pp. 84-92.
Elsevier DOI 0809
Taxonomy; Decision tree; Propositionalization; Jensen-Shannon divergence measure BibRef

Nichol, J.[Janet], Wong, M.S.[Man Sing],
Habitat Mapping in Rugged Terrain Using Multispectral Ikonos Images,
PhEngRS(74), No. 11, November 2008, pp. 1325-1334.
WWW Link. 0804
A multi-level object-oriented and decision-tree classifier for detailed habitat mapping in rugged terrain. BibRef

Ouyang, J.[Jie], Patel, N.[Nilesh], Sethi, I.[Ishwar],
Induction of multiclass multifeature split decision trees from distributed data,
PR(42), No. 9, September 2009, pp. 1786-1794.
Elsevier DOI 0905
Distributed data mining; Decision tree; Fisher linear discriminant BibRef

Liu, J.[Jing], Li, X.[Xue], Zhong, W.[Weicai],
Ambiguous decision trees for mining concept-drifting data streams,
PRL(30), No. 15, 1 November 2009, pp. 1347-1355.
Elsevier DOI 0910
Data streams; Data mining; Concept drift; Ambiguous decision trees; Incremental learning BibRef

Chandra, B., Kothari, R.[Ravi], Paul, P.[Pallath],
A new node splitting measure for decision tree construction,
PR(43), No. 8, August 2010, pp. 2725-2731.
Elsevier DOI 1006
Decision trees; Node splitting measure; Gini Index; Gain Ratio BibRef

Baraldi, A., Wassenaar, T., Kay, S.,
Operational Performance of an Automatic Preliminary Spectral Rule-Based Decision-Tree Classifier of Spaceborne Very High Resolution Optical Images,
GeoRS(48), No. 9, September 2010, pp. 3482-3502.
IEEE DOI 1008
BibRef

Baraldi, A., Puzzolo, V., Blonda, P., Bruzzone, L., Tarantino, C.,
Automatic Spectral Rule-Based Preliminary Mapping of Calibrated Landsat TM and ETM+ Images,
GeoRS(44), No. 9, September 2006, pp. 2563-2586.
IEEE DOI 0609
BibRef

Baraldi, A.,
Fuzzification of a Crisp Near-Real-Time Operational Automatic Spectral-Rule-Based Decision-Tree Preliminary Classifier of Multisource Multispectral Remotely Sensed Images,
GeoRS(49), No. 6, June 2011, pp. 2113-2134.
IEEE DOI 1106
BibRef

Fujiyoshi, A.[Akio], Suzuki, M.[Masakazu],
Minimum Spanning Tree Problem with Label Selection,
IEICE(E94-D), No. 2, February 2011, pp. 233-239.
WWW Link. 1102
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Douzal-Chouakria, A.[Ahlame], Amblard, C.[Cécile],
Classification trees for time series,
PR(45), No. 3, March 2012, pp. 1076-1091.
Elsevier DOI 1111
Time series proximity measures; Supervised classification; Classification trees; Learning metric BibRef

Moustakidis, S., Mallinis, G., Koutsias, N., Theocharis, J.B., Petridis, V.,
SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images,
GeoRS(50), No. 1, January 2012, pp. 149-169.
IEEE DOI 1201
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Rodriguez-Lujan, I.[Irene], Cruz, C.S.[Carlos Santa], Huerta, R.[Ramon],
Hierarchical linear support vector machine,
PR(45), No. 12, December 2012, pp. 4414-4427.
Elsevier DOI 1208
Large-scale learning; Real-time prediction; Support vector machine; Decision tree; Pegasos algorithm BibRef

Mehenni, T.[Tahar], Moussaoui, A.[Abdelouahab],
Data mining from multiple heterogeneous relational databases using decision tree classification,
PRL(33), No. 13, 1 October 2012, pp. 1768-1775.
Elsevier DOI 1208
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And: Corrigendum: PRL(40), No. 1, 2014, pp. 136-.
Elsevier DOI 1403
Heterogeneous relational databases; Multi-database mining; Multi-relational classification; Inter-database links; Link usefulness BibRef

Grana, C.[Costantino], Montangero, M.[Manuela], Borghesani, D.[Daniele],
Optimal decision trees for local image processing algorithms,
PRL(33), No. 16, 1 December 2012, pp. 2302-2310.
Elsevier DOI 1210
Decision trees; Decision tables; Connected components labeling; Thinning BibRef

Lefort, R.[Riwal], Fleuret, F.[François],
treeKL: A distance between high dimension empirical distributions,
PRL(34), No. 2, 15 January 2013, pp. 140-145.
Elsevier DOI 1212
Kulback-Leibler distance; Unsupervised trees; Distribution modeling BibRef

Criminisi, A., Shotton, J.D.J., (Eds.)
Decision Forests for Computer Vision and Medical Image Analysis,
Springer2013. ISBN 978-1-4471-4928-6.


WWW Link. 1304
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Kontschieder, P.[Peter], Kohli, P.[Pushmeet], Shotton, J.D.J.[Jamie D.J.], Criminisi, A.[Antonio],
GeoF: Geodesic Forests for Learning Coupled Predictors,
CVPR13(65-72)
IEEE DOI 1309
Decision forests BibRef

Proctor, C.[Cameron], He, Y.H.[Yu-Hong], Robinson, V.[Vincent],
Texture augmented detection of macrophyte species using decision trees,
PandRS(80), No. 1, June 2013, pp. 10-20.
Elsevier DOI 1305
Floating macrophytes; Image texture; Feature selection; Jefferies-Matusita distance; Decision trees BibRef

Hu, H.W.[Hsiao-Wei], Chen, Y.L.[Yen-Liang], Tang, K.[Kwei],
A Novel Decision-Tree Method for Structured Continuous-Label Classification,
Cyber(43), No. 6, 2013, pp. 1734-1746.
IEEE DOI 1312
computational complexity BibRef

Bao, L.C.[Lin-Chao], Song, Y.B.[Yi-Bing], Yang, Q.X.[Qing-Xiong], Yuan, H.[Hao], Wang, G.[Gang],
Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree,
IP(23), No. 2, February 2014, pp. 555-569.
IEEE DOI 1402
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And: Erratum: IP(23), No. 6, June 2014, pp. 2751-2751.
IEEE DOI 1406
computational complexity BibRef

Johnson, R., Zhang, T.,
Learning Nonlinear Functions Using Regularized Greedy Forest,
PAMI(36), No. 5, May 2014, pp. 942-954.
IEEE DOI 1405
Additives BibRef

Criminisi, A.[Antonio], Shotton, J.[Jamie], Konukoglu, E.[Ender],
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning,
FTCGV(7), Issue 2-3, 2011, pp. 81-277.
DOI Link 1410
Published March 2012. BibRef

Wang, X.C.[Xian-Chang], Liu, X.D.[Xiao-Dong], Pedrycz, W.[Witold], Zhang, L.[Lishi],
Fuzzy rule based decision trees,
PR(48), No. 1, 2015, pp. 50-59.
Elsevier DOI 1410
Decision tree BibRef

Kang, P.[Pilsung], Cho, S.Z.[Sung-Zoon],
Locally linear reconstruction for instance-based learning,
PR(41), No. 11, November 2008, pp. 3507-3518.
Elsevier DOI 0808
Instance-based learning; Memory-based reasoning; k-nearest neighbor; Weight allocation; Local reconstruction BibRef

Fan, J.P.[Jian-Ping], Zhang, J.[Ji], Mei, K.[Kuizhi], Peng, J.Y.[Jin-Ye], Gao, L.[Ling],
Cost-sensitive learning of hierarchical tree classifiers for large-scale image classification and novel category detection,
PR(48), No. 5, 2015, pp. 1673-1687.
Elsevier DOI 1502
Large-scale image classification BibRef

Zheng, Y.[Yu], Fan, J.P.[Jian-Ping], Zhang, J.[Ji], Gao, X.B.[Xin-Bo],
Hierarchical learning of multi-task sparse metrics for large-scale image classification,
PR(67), No. 1, 2017, pp. 97-109.
Elsevier DOI 1704
Hierarchical multi-task sparse metric learning BibRef

Fan, J.P.[Jian-Ping], Zhou, N.[Ning], Peng, J.Y.[Jin-Ye], Gao, L.[Ling],
Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification,
IP(24), No. 11, November 2015, pp. 4172-4184.
IEEE DOI 1509
image classification BibRef

Weng, C., Yuan, J.,
Efficient Mining of Optimal AND/OR Patterns for Visual Recognition,
MultMed(17), No. 5, May 2015, pp. 626-635.
IEEE DOI 1505
Benchmark testing BibRef

Sok, H.K.[Hong Kuan], Ooi, M.P.L.[Melanie Po-Leen], Kuang, Y.C.[Ye Chow],
Sparse alternating decision tree,
PRL(60-61), No. 1, 2015, pp. 57-64.
Elsevier DOI 1506
Alternating decision tree BibRef

Sok, H.K.[Hong Kuan], Ooi, M.P.L.[Melanie Po-Leen], Kuang, Y.C.[Ye Chow], Demidenko, S.[Serge],
Multivariate alternating decision trees,
PR(50), No. 1, 2016, pp. 195-209.
Elsevier DOI 1512
Alternating decision tree BibRef

Liu, G.L.[Gui-Long], Li, L.[Ling], Yang, J.[Jitao], Feng, Y.B.[Yan-Bin], Zhu, K.[Kai],
Attribute reduction approaches for general relation decision systems,
PRL(65), No. 1, 2015, pp. 81-87.
Elsevier DOI 1511
Attribute reduction BibRef

Liu, D.H.[De-Hua], Zhang, P.[Peng], Zheng, Q.H.[Qing-Hua],
An efficient online active learning algorithm for binary classification,
PRL(68, Part 1), No. 1, 2015, pp. 22-26.
Elsevier DOI 1512
Online active learning BibRef

Ma, L.[Liyao], Destercke, S.[Sébastien], Wang, Y.[Yong],
Online active learning of decision trees with evidential data,
PR(52), No. 1, 2016, pp. 33-45.
Elsevier DOI 1601
Decision tree BibRef

Kim, K.[Kyoungok],
A hybrid classification algorithm by subspace partitioning through semi-supervised decision tree,
PR(60), No. 1, 2016, pp. 157-163.
Elsevier DOI 1609
Decision tree BibRef

Zhou, G.Q.[Guo-Qing], Zhang, R.T.[Rong-Ting], Zhang, D.[Dianjun],
Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification,
RS(8), No. 10, 2016, pp. 855.
DOI Link 1609
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Liu, R.Z., Fang, B., Luo, H.W.,
Automatic decision support by rule exhaustion decision tree algorithm,
ICWAPR16(25-30)
IEEE DOI 1611
Algorithm design and analysis BibRef

Ghattas, B.[Badih], Michel, P.[Pierre], Boyer, L.[Laurent],
Clustering nominal data using unsupervised binary decision trees: Comparisons with the state of the art methods,
PR(67), No. 1, 2017, pp. 177-185.
Elsevier DOI 1704
CUBT BibRef

Yang, C.[Chao], Wu, G.F.[Guo-Feng], Ding, K.[Kai], Shi, T.Z.[Tie-Zhu], Li, Q.Q.[Qing-Quan], Wang, J.L.[Jin-Liang],
Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
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Cousty, J.[Jean], Najman, L.[Laurent], Kenmochi, Y.[Yukiko], Guimarães, S.[Silvio],
Hierarchical Segmentations with Graphs: Quasi-flat Zones, Minimum Spanning Trees, and Saliency Maps,
JMIV(60), No. 4, May 2018, pp. 479-502.
Springer DOI 1804
BibRef
Earlier:
New Characterizations of Minimum Spanning Trees and of Saliency Maps Based on Quasi-flat Zones,
ISMM15(205-216).
Springer DOI 1506
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Turrisi da Costa, V.G.[Victor Guilherme], Ponce de Leon Ferreira de Carvalho, A.C.[André Carlos], Junior, S.B.[Sylvio Barbon],
Strict Very Fast Decision Tree: A memory conservative algorithm for data stream mining,
PRL(116), 2018, pp. 22-28.
Elsevier DOI 1812
Data stream mining, Machine learning, Memory-friendly algorithm BibRef

Danda, S.[Sravan], Challa, A.[Aditya], Sagar, B.S.D.[B. S. Daya], Najman, L.[Laurent],
Some Theoretical Links Between Shortest Path Filters and Minimum Spanning Tree Filters,
JMIV(61), No. 6, July 2019, pp. 745-762.
Springer DOI 1907
BibRef
Earlier:
Power Tree Filter: A Theoretical Framework Linking Shortest Path Filters and Minimum Spanning Tree Filters,
ISMM17(199-210).
Springer DOI 1706
BibRef

Adibi, M.A.[Mohammad Amin],
Single and multiple outputs decision tree classification using bi-level discrete-continues genetic algorithm,
PRL(128), 2019, pp. 190-196.
Elsevier DOI 1912
Classification, Discrete-continues genetic algorithm, Bi-level optimization, Genetic operators, Decision tree, Multiple outputs data BibRef

Hehn, T.M.[Thomas M.], Kooij, J.F.P.[Julian F. P.], Hamprecht, F.A.[Fred A.],
End-to-End Learning of Decision Trees and Forests,
IJCV(128), No. 4, April 2020, pp. 997-1011.
Springer DOI 2004
BibRef
Earlier: A1, A3, Only:
End-to-End Learning of Deterministic Decision Trees,
GCPR18(612-627).
Springer DOI 1905
Award, GCPR. BibRef

Dong, M.Q.[Man-Qing], Yao, L.[Lina], Wang, X.Z.[Xian-Zhi], Benatallah, B.[Boualem], Huang, C.R.[Chao-Ran], Ning, X.D.[Xiao-Dong],
Opinion fraud detection via neural autoencoder decision forest,
PRL(132), 2020, pp. 21-29.
Elsevier DOI 2005
Autoencoder, Neural decision forest, Opinion fraud detection BibRef

Mu, Y.S.[Ya-Shuang], Liu, X.D.[Xiao-Dong], Wang, L.D.[Li-Dong], Zhou, J.I.[Jux-Iang],
A parallel fuzzy rule-base based decision tree in the framework of map-reduce,
PR(103), 2020, pp. 107326.
Elsevier DOI 2005
Parallel computing, Fuzzy classifier, Decision trees, Fuzzy rules, Map-Reduce BibRef

Wang, F.[Fei], Wang, Q.[Quan], Nie, F.P.[Fei-Ping], Li, Z.H.[Zhong-Heng], Yu, W.Z.[Wei-Zhong], Ren, F.[Fuji],
A linear multivariate binary decision tree classifier based on K-means splitting,
PR(107), 2020, pp. 107521.
Elsevier DOI 2008
Hierarchical classifier, Binary tree, Multivariate decision tree, K-means, Supervised classification BibRef

Wang, B.[Bo], Chen, Y.L.[Yi-Liang], Liu, W.X.[Wen-Xi], Qin, J.[Jing], Du, Y.[Yong], Han, G.Q.[Guo-Qiang], He, S.F.[Sheng-Feng],
Real-Time Hierarchical Supervoxel Segmentation via a Minimum Spanning Tree,
IP(29), 2020, pp. 9665-9677.
IEEE DOI 2011
Heuristic algorithms, Task analysis, Spatiotemporal phenomena, Real-time systems, Object segmentation, minimum spanning tree BibRef

Tu, W.C.[Wei-Chih], He, S.F.[Sheng-Feng], Yang, Q.X.[Qing-Xiong], Chien, S.Y.[Shao-Yi],
Real-Time Salient Object Detection with a Minimum Spanning Tree,
CVPR16(2334-2342)
IEEE DOI 1612
BibRef

Rahman, M.G.[Md Geaur], Islam, M.Z.[Md Zahidul],
Adaptive Decision Forest: An incremental machine learning framework,
PR(122), 2022, pp. 108345.
Elsevier DOI 2112
Incremental learning, Decision forest algorithm, Concept drift, Big data, Online learning BibRef

Xue, M.Q.[Meng-Qi], Zhang, H.F.[Hao-Fei], Huang, Q.[Qihan], Song, J.[Jie], Song, M.L.[Ming-Li],
Learn decision trees with deep visual primitives,
JVCIR(89), 2022, pp. 103682.
Elsevier DOI 2212
Interpretability, Deep neural network, Discrete representation learning BibRef

Chen, S.[Song], Zhang, F.[Fuhao], Zhang, Z.R.[Zhi-Ran], Yu, S.Y.[Si-Yi], Qiu, A.[Agen], Liu, S.Q.[Shang-Qin], Zhao, X.Z.[Xi-Zhi],
Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree,
IJGI(12), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Nanfack, G.[Géraldin], Temple, P.[Paul], Frénay, B.[Benoît],
Learning Customised Decision Trees for Domain-knowledge Constraints,
PR(142), 2023, pp. 109610.
Elsevier DOI 2307
Decision trees, Constraints, Domain knowledge BibRef

Gao, B.J.[Bing-Jie], Zhou, Q.L.[Qian-Li], Deng, Y.[Yong],
HIE-EDT: Hierarchical interval estimation-based evidential decision tree,
PR(146), 2024, pp. 110040.
Elsevier DOI 2311
Evidential decision tree, Dempster-Shafer theory, Hierarchical interval estimation, Classification, Fractal-based belief entropy BibRef


Carreira-Perpiñán, M.Á.[Miguel Á.], Gabidolla, M.[Magzhan], Zharmagambetov, A.[Arman],
Towards Better Decision Forests: Forest Alternating Optimization,
CVPR23(7589-7598)
IEEE DOI 2309
BibRef

Irsoy, O.[Ozan], Alpaydin, E.[Ethem],
Distributed Decision Trees,
SSSPR22(152-162).
Springer DOI 2301
BibRef

Manzali, Y.[Youness], El Far, P.M.[Pr. Mohamed],
A new decision tree pre-pruning method based on nodes probabilities,
ISCV22(1-5)
IEEE DOI 2208
Machine learning, Benchmark testing, Decision trees, Noise measurement, Data mining, node probabilities BibRef

Zharmagambetov, A.[Arman], Carreira-Perpiñán, M.Á.[Miguel Á.],
A Simple, Effective Way To Improve Neural Net Classification: Ensembling Unit Activations With A Sparse Oblique Decision Tree,
ICIP21(369-373)
IEEE DOI 2201
Training, Runtime, Neural networks, Vegetation, Feature extraction, Prediction algorithms, Classification algorithms, feature extraction BibRef

Hada, S.S.[Suryabhan Singh], Carreira-Perpiñán, M.Á.[Miguel Á.], Zharmagambetov, A.[Arman],
Understanding and Manipulating Neural Net Features Using Sparse Oblique Classification Trees,
ICIP21(3707-3711)
IEEE DOI 2201
Training, Histograms, Image processing, Neurons, Neural networks, Vegetation, interpretability, deep nets, decision trees BibRef

Alaniz, S.[Stephan], Marcos, D.[Diego], Schiele, B.[Bernt], Akata, Z.[Zeynep],
Learning Decision Trees Recurrently Through Communication,
CVPR21(13513-13522)
IEEE DOI 2111
Recurrent neural networks, Computational modeling, Scalability, Message passing, Semantics, Decision making, Predictive models BibRef

Song, J.[Jie], Zhang, H.F.[Hao-Fei], Wang, X.C.[Xin-Chao], Xue, M.Q.[Meng-Qi], Chen, Y.[Ying], Sun, L.[Li], Tao, D.C.[Da-Cheng], Song, M.L.[Ming-Li],
Tree-like Decision Distillation,
CVPR21(13483-13492)
IEEE DOI 2111
Knowledge engineering, Cats, Decision making, Dogs, Pattern recognition, Problem-solving BibRef

Welke, P.[Pascal], Alkhoury, F.[Fouad], Bauckhage, C.[Christian], Wrobel, S.[Stefan],
Decision Snippet Features,
ICPR21(4260-4267)
IEEE DOI 2105
Performance evaluation, Predictive models, Hardware, Decision trees, Random forests BibRef

La Grassa, R.[Riccardo], Gallo, I.[Ignazio], Calefati, A.[Alessandro], Ognibene, D.[Dimitri],
Binary Classification Using Pairs of Minimum Spanning Trees or N-Ary Trees,
CAIP19(II:365-376).
Springer DOI 1909
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Oropeza, M.[Marcos], Tóth, C.D.[Csaba D.],
Reconstruction of the Crossing Type of a Point Set from the Compatible Exchange Graph of Noncrossing Spanning Trees,
DGCI19(234-245).
Springer DOI 1905
BibRef

Garhwal, A.S.[Abhimanyu Singh], Yan, W.Q.[Wei Qi], Narayanan, A.[Ajit],
Image phylogeny for simulating multiple print-scan,
IVCNZ17(1-6)
IEEE DOI 1902
bioinformatics, DNA, genetics, image coding, sequences, trees (mathematics), image phylogeny, multiple print-scan, image phylogeny BibRef

Gigli, L., Velasco-Forero, S., Marcotegui, B.,
On Minimum Spanning Tree Streaming for Image Analysis,
ICIP18(3229-3233)
IEEE DOI 1809
Image edge detection, Streaming media, Image segmentation, Remote sensing, Image analysis, Morphology, Minimum Spanning Tree, Hierarchical Segmentation BibRef

Elaidi, H., Elhaddar, Y., Benabbou, Z., Abbar, H.,
An idea of a clustering algorithm using support vector machines based on binary decision tree,
ISCV18(1-5)
IEEE DOI 1807
decision trees, learning (artificial intelligence), pattern clustering, support vector machines, BDT, SVM, unsupervised learning BibRef

Lazo-Cortés, M.S.[Manuel S.], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[Jesús Ariel],
Class-Specific Reducts vs. Classic Reducts in a Rule-Based Classifier: A Case Study,
MCPR18(23-30).
Springer DOI 1807
BibRef

Babenko, A.[Artem], Lempitsky, V.[Victor],
Product Split Trees,
CVPR17(6316-6324)
IEEE DOI 1711
Databases, Memory management, Nearest neighbor searches, Partitioning algorithms, Reactive power, Vegetation BibRef

Zheng, X.W.[Xian-Wei], Tang, Y.Y.[Yuan Yan], Zhou, J.T.[Jian-Tao], Wang, P.S.[Patrick S.],
Maximal level estimation and unbalance reduction for graph signal downsampling,
ICPR16(3922-3926)
IEEE DOI 1705
Fourier transforms, Laplace equations, Pattern recognition, Roads, Signal processing, Social network services BibRef

Ohn-Bar, E.[Eshed], Trivedi, M.M.[Mohan Manubhai],
To boost or not to boost? On the limits of boosted trees for object detection,
ICPR16(3350-3355)
IEEE DOI 1705
Boosting, Decision trees, Detectors, Feature extraction, Object detection, Training BibRef

Liu, Y.[Yang], Huang, L., Wang, S., Liu, X., Lang, B.,
Efficient segmentation for Region-based Image Retrieval using Edge Integrated Minimum Spanning Tree,
ICPR16(1929-1934)
IEEE DOI 1705
Feature extraction, Image edge detection, Image retrieval, Image segmentation, Semantics, Vegetation, Visualization BibRef

Rota Bulò, S.[Samuel], Kontschieder, P.[Peter],
Online Learning with Bayesian Classification Trees,
CVPR16(3985-3993)
IEEE DOI 1612
BibRef

Yang, Q.Q.[Qing-Qing], Wang, L.[Lang], Wang, Y.G.[Yi-Gang], Fan, S.L.[Sheng-Li],
Fast weighted cost propagation with smoothness constraint on a tree,
ICIP16(3459-3463)
IEEE DOI 1610
Computational complexity. Spanning tree. BibRef

Verma, A.K.[Amit Kumar], Garg, P.K., Prasad, K.S.H.[K. S. Hari], Dadhwal, V.K.,
Classification of Liss IV Imagery Using Decision Tree Methods,
ISPRS16(B8: 1061-1066).
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Gundogdu, E., Koç, A., Alatan, A.A.,
Object classification in infrared images using deep representations,
ICIP16(1066-1070)
IEEE DOI 1610
Decision trees BibRef

Pan, P.[Pan], Zhai, J.H.[Jun-Hai], Chen, W.[Wu],
An improved ordinal decision tree induction algorithm,
ICWAPR15(220-224)
IEEE DOI 1511
decision trees BibRef

Wang, X.[Xin], Zhai, J.H.[Jun-Hai], Chen, J.[Jiankai], Wang, X.[Xizhao],
Ordinal decision trees based on fuzzy rank entropy,
ICWAPR15(208-213)
IEEE DOI 1511
decision trees BibRef

Zhang, J.[Jian], Zhai, J.H.[Jun-Hai], Zhu, H.[Hong], Wang, X.[Xizhao],
Induction of monotonic decision trees,
ICWAPR15(203-207)
IEEE DOI 1511
decision trees BibRef

Guyet, T.,
Extracting characteristics of satellite image time series with decision trees,
MultiTemp15(1-4)
IEEE DOI 1511
decision trees BibRef

Lassner, C.[Christoph], Lienhart, R.[Rainer],
Norm-Induced Entropies for Decision Forests,
WACV15(968-975)
IEEE DOI 1503
Context; Entropy; Equations; Training; Vectors; Vegetation BibRef

Skurikhin, A.N.[Alexei N.],
Learning tree-structured approximations for conditional random fields,
AIPR14(1-8)
IEEE DOI 1504
BibRef
And:
Hierarchical Spanning Tree-Structured Approximation for Conditional Random Fields: An Empirical Study,
ISVC14(II: 85-94).
Springer DOI 1501
Markov processes BibRef

Yildiz, O.T.[Olcay Taner],
VC-Dimension of Rule Sets,
ICPR14(3576-3581)
IEEE DOI 1412
Computers BibRef

Irsoy, O.[Ozan], Yildiz, O.T.[Olcay Taner], Alpaydin, E.[Ethem],
Budding Trees,
ICPR14(3582-3587)
IEEE DOI 1412
Accuracy BibRef

Unda-Trillas, E.[Emilio], Rivera-Rovelo, J.[Jorge],
A Method to Build Classification and Regression Trees,
CIARP14(448-453).
Springer DOI 1411
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Laptev, D.[Dmitry], Savinov, N., Buhmann, J.M.[Joachim M.], Pollefeys, M.,
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CVPR16(289-297)
IEEE DOI 1612
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Laptev, D.[Dmitry], Buhmann, J.M.[Joachim M.],
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CVPR15(3043-3051)
IEEE DOI 1510
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Springer DOI 1411
Award, GCPR, HM. BibRef

Ylioinas, J.[Juha], Kannala, J.H.[Ju-Ho], Hadid, A.[Abdenour], Pietikainen, M.[Matti],
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WACV14(347-354)
IEEE DOI 1406
Accuracy; Decision trees; Entropy; Geometry; Materials; Robustness; Training BibRef

Xia, Y.[Yan], He, K.M.[Kai-Ming], Wen, F.[Fang], Sun, J.[Jian],
Joint Inverted Indexing,
ICCV13(3416-3423)
IEEE DOI 1403
Large scale search. BibRef

Kahler, O.[Olaf], Reid, I.D.[Ian D.],
Efficient 3D Scene Labeling Using Fields of Trees,
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Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees,
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Lazo-Cortés, M.S.[Manuel S.], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[Jesús Ariel],
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image classification BibRef

Zhong, C.M.[Cai-Ming], Malinen, M.[Mikko], Miao, D.Q.[Duo-Qian],
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Yang, Y.L.[Ya-Ling], Wang, H.H.[Hai-Hui], Zeng, K.[Kun], Lv, H.[Han], Li, S.S.[Shan-Shan],
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Garcia-Gutierrez, J.[Jorge], Gonçalves-Seco, L.[Luis], Riquelme-Santos, J.C.[Jose C.],
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de Sá, J.P.M.[J. P. Marques], Sebastião, R.[Raquel], Gama, J.[João], Fontes, T.[Tânia],
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de Sá, J.P.M.[J. P. Marques], Gama, J.[João], Sebastião, R.[Raquel], Alexandre, L.A.[Luís A.],
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Dictionary-free categorization of very similar objects via stacked evidence trees,
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IEEE DOI 0906
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Aziere, N.[Nicolas], Todorovic, S.[Sinisa],
Ensemble Deep Manifold Similarity Learning Using Hard Proxies,
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IEEE DOI 2002
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Basak, J.[Jayanta],
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ICPR08(1-4).
IEEE DOI 0812
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Sharp, T.[Toby],
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Haynes, K., Liu, X.W.[Xiu-Wen], Mio, W.,
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ICIP06(2753-2756).
IEEE DOI 0610
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Isukapalli, R.[Ramana], Elgammal, A.M.[Ahmed M.],
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Lee, S.T.[Seng-Tai], Kim, J.[Jeehoon], Baek, J.Y.[Jae-Yeon], Han, M.W.[Man-Wi], Kim, S.S.[Sung-Shin], Chon, T.S.[Tae-Soo],
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Aguirre, M.D., Barner, K.E.,
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ICIP00(Vol I: 924-927).
IEEE DOI 0008
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Salvador-Sanchez, J.[Jose], Pla, F.[Filiberto], Ferri, F.J.[Francesc J.],
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Mottl, V.[Vadim], Kostin, A., Muchnik, I., Blinov, A., Kopylov, A.,
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Shepherd, B.A.,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Random Forests Classification .


Last update:Oct 22, 2024 at 22:09:59