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,
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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
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Earlier:
Automated design of piecewise-linear classifiers of multiple-class data,
ICPR88(II: 1068-1071).
IEEE DOI 8811
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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
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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
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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
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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
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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.[Shuyuan], 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.[Linchao], 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
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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.[Xinbo],
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
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
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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

Danda, S.[Sravan], Challa, A.[Aditya], Sagar, B.S.D.[B. S. Daya], Najman, L.[Laurent],
Power Tree Filter: A Theoretical Framework Linking Shortest Path Filters and Minimum Spanning Tree Filters,
ISMM17(199-210).
Springer DOI 1706
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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, Computer architecture, 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

Tu, W.C., He, S., Yang, Q., Chien, S.Y.,
Real-Time Salient Object Detection with a Minimum Spanning Tree,
CVPR16(2334-2342)
IEEE DOI 1612
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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).
DOI Link 1610
BibRef

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

Narendra, N.P., Rao, K.S.[K. Sreenivasa],
Optimal residual frame based source modeling for HMM-based speech synthesis,
ICAPR15(1-5)
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
Computer vision; 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.,
TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks,
CVPR16(289-297)
IEEE DOI 1612
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Laptev, D.[Dmitry], Buhmann, J.M.[Joachim M.],
Transformation-Invariant Convolutional Jungles,
CVPR15(3043-3051)
IEEE DOI 1510
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Earlier:
Convolutional Decision Trees for Feature Learning and Segmentation,
GCPR14(95-106).
Springer DOI 1411
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Ylioinas, J.[Juha], Kannala, J.H.[Ju-Ho], Hadid, A.[Abdenour], Pietikainen, M.[Matti],
Learning local image descriptors using binary decision trees,
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,
ICCV13(3064-3071)
IEEE DOI 1403
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Aodha, O.M.[Oisin Mac], Brostow, G.J.[Gabriel J.],
Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees,
ICCV13(193-200)
IEEE DOI 1403
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Lazo-Cortés, M.S.[Manuel S.], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[Jesús Ariel],
On Two Definitions of Reduct,
MCPR14(31-40).
Springer DOI 1407
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Hernández-León, R.[Raudel], Hernández-Palancar, J.[José], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José Francisco],
Studying Netconf in Hybrid Rule Ordering Strategies for Associative Classification,
MCPR14(51-60).
Springer DOI 1407
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Lazo-Cortés, M.S.[Manuel S.], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[Jesús Ariel],
Easy Categorization of Attributes in Decision Tables Based on Basic Binary Discernibility Matrix,
CIARP13(I:302-310).
Springer DOI 1311
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Liu, B.Y.[Bao-Yuan], Sadeghi, F.[Fereshteh], Tappen, M.[Marshall], Shamir, O.[Ohad], Liu, C.[Ce],
Probabilistic Label Trees for Efficient Large Scale Image Classification,
CVPR13(843-850)
IEEE DOI 1309
image classification BibRef

Zhong, C.M.[Cai-Ming], Malinen, M.[Mikko], Miao, D.Q.[Duo-Qian],
Fast Approximate Minimum Spanning Tree Algorithm Based on K-Means,
CAIP13(262-269).
Springer DOI 1308
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Irsoy, O.[Ozan], Yildiz, O.T.[Olcay Taner], Alpaydin, E.[Ethem],
Soft decision trees,
ICPR12(1819-1822).
WWW Link. 1302
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Fast Approximations to Structured Sparse Coding and Applications to Object Classification,
ECCV12(V: 200-213).
Springer DOI 1210
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Müller, A.C.[Andreas C.], Nowozin, S.[Sebastian], Lampert, C.H.[Christoph H.],
Information Theoretic Clustering Using Minimum Spanning Trees,
DAGM12(205-215).
Springer DOI 1209
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Jancsary, J.[Jeremy], Nowozin, S.[Sebastian], Sharp, T.[Toby], Rother, C.[Carsten],
Regression Tree Fields: An efficient, non-parametric approach to image labeling problems,
CVPR12(2376-2383).
IEEE DOI 1208
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Manwani, N.[Naresh], Sastry, P.S.,
A Geometric Algorithm for Learning Oblique Decision Trees,
PReMI09(25-31).
Springer DOI 0912
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Patvardhan, C., Prakash, V.P.[V. Prem],
Novel Deterministic Heuristics for Building Minimum Spanning Trees with Constrained Diameter,
PReMI09(68-73).
Springer DOI 0912
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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],
A Tree-Structure Classifier Ensemble for Tracked Target Categorization,
CISP09(1-5).
IEEE DOI 0910
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Li, W.L.[Wen-Long], Liang, T.G.[Tian-Gang], Wang, X.[Xun],
Remote Sensing Image Extraction and Precision Analysis for Alpine Wetland Based on Coupling Analysis of Multispectral Factor PCA and Decision Tree,
CISP09(1-6).
IEEE DOI 0910
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Garcia-Gutierrez, J.[Jorge], Gonçalves-Seco, L.[Luis], Riquelme-Santos, J.C.[Jose C.],
Decision Trees on Lidar to Classify Land Uses and Covers,
Laser09(323). 0909
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de Sá, J.P.M.[J. P. Marques], Sebastião, R.[Raquel], Gama, J.[João], Fontes, T.[Tânia],
New Results on Minimum Error Entropy Decision Trees,
CIARP11(355-362).
Springer DOI 1111
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de Sá, J.P.M.[J. P. Marques], Gama, J.[João], Sebastião, R.[Raquel], Alexandre, L.A.[Luís A.],
Decision Trees Using the Minimum Entropy-of-Error Principle,
CAIP09(799-807).
Springer DOI 0909
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Martinez-Munoz, G.[Gonzalo], Larios, N.[Natalia], Mortensen, E.N.[Eric N.], Zhang, W.[Wei], Yamamuro, A.[Asako], Paasch, R.[Robert], Payet, N.[Nadia], Lytle, D.A.[David A.], Shapiro, L.G.[Linda G.], Todorovic, S.[Sinisa], Moldenke, A.[Andrew], Dietterich, T.G.[Thomas G.],
Dictionary-free categorization of very similar objects via stacked evidence trees,
CVPR09(549-556).
IEEE DOI 0906
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Basak, J.[Jayanta],
Online adaptive clustering in a decision tree framework,
ICPR08(1-4).
IEEE DOI 0812
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Zhong, M.Y.[Ming-Yu], Georgiopoulos, M.[Michael], Anagnostopoulos, G.C.[Georgios C.],
Properties of the k-norm pruning algorithm for decision tree classifiers,
ICPR08(1-4).
IEEE DOI 0812
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Sharp, T.[Toby],
Implementing Decision Trees and Forests on a GPU,
ECCV08(IV: 595-608).
Springer DOI 0810
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Haynes, K., Liu, X.W.[Xiu-Wen], Mio, W.,
Recognition using Rapid Classification Tree,
ICIP06(2753-2756).
IEEE DOI 0610
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Isukapalli, R.[Ramana], Elgammal, A.M.[Ahmed M.],
Learning Policies for Efficiently Identifying Objects of Many Classes,
ICPR06(III: 356-361).
IEEE DOI 0609
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Isukapalli, R.[Ramana], Elgammal, A.M.[Ahmed M.], Greiner, R.[Russell],
Learning to Detect Objects of Many Classes Using Binary Classifiers,
ECCV06(I: 352-364).
Springer DOI 0608
Create a decision tree classifier, where each node is based on Viola-Jones ( See also Robust Real-Time Face Detection. ). BibRef

Gangaputra, S.[Sachin], Geman, D.[Donald],
A Design Principle for Coarse-to-Fine Classification,
CVPR06(II: 1877-1884).
IEEE DOI 0606
Nested representation of binary classifiers. BibRef

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],
Pattern Analysis of Movement Behavior of Medaka (Oryzias latipes) A Decision Tree Approach,
CAIP05(546).
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Lee, J.S.[Jin-Seon], Oh, I.S.[Il-Seok],
Binary classification trees for multi-class classification problems,
ICDAR03(770-774).
IEEE DOI 0311
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Cho, S.Y.[Siu-Yeung], Chi, Z.[Zheru], Wang, Z.Y.[Zhi-Yong], Siu, W.C.[Wan-Chi],
Robust learning in adaptive processing of data structures for tree representation based image classification,
ICPR02(II: 108-111).
IEEE DOI 0211
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Aguirre, M., Barner, K.,
Multiresolution Permutation Filters Based on Decision Trees,
ICIP00(Vol I: 924-927).
IEEE DOI 0008
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Salvador-Sanchez, J.[Jose], Pla, F.[Filiberto], Ferri, F.J.[Francesc J.],
A Voronoi-Diagram-Based Approach to Oblique Decision Tree Induction,
ICPR98(Vol I: 542-544).
IEEE DOI 9808
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Gracia, I., Pla, F., Ferri, F.J., García, P.,
Estimating feature discriminant power in decision tree classifiers,
CAIP95(612-617).
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Lange, M., Ganebnykh, S.,
Tree-like data structures for effective recognition of 2-D solids,
ICPR04(I: 592-595).
IEEE DOI 0409
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Lange, M.M.[Michael M.],
Fast Pattern Recognition on a Base of Recursive Representation with Binary Trees,
SCIA97(xx-yy)
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Imiya, A., Fujiwara, Y.,
Reconstruction, Recognition, and Representation of Trees,
ICPR96(I: 595-600).
IEEE DOI 9608
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Yoshii, H.,
Pyramid Architecture Classification Tree,
ICPR96(II: 310-314).
IEEE DOI 9608
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Mottl, V.[Vadim], Kostin, A., Muchnik, I., Blinov, A., Kopylov, A.,
Variational Methods in Signal and Image Analysis,
ICPR98(Vol I: 525-527).
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Mottl, V., Muchnik, I., Blinov, A., Kopylov, A.,
Hidden Tree-Like Quasi-Markov Model and Generalized Technique for a Class of Image Processing Problems,
ICPR96(II: 715-719).
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Vorácek, J.[Jan],
Tree neural classifier for character recognition,
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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:Aug 16, 2018 at 18:22:30