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PR(50), No. 1, 2016, pp. 195-209.
Elsevier DOI
1512
Alternating decision tree
BibRef
Liu, G.L.[Gui-Long],
Li, L.[Ling],
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Feng, Y.B.[Yan-Bin],
Zhu, K.[Kai],
Attribute reduction approaches for general relation decision systems,
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Elsevier DOI
1511
Attribute reduction
BibRef
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PRL(68, Part 1), No. 1, 2015, pp. 22-26.
Elsevier DOI
1512
Online active learning
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Ma, L.[Liyao],
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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
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Elsevier DOI
1609
Decision tree
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Zhou, G.Q.[Guo-Qing],
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DOI Link
1609
BibRef
Liu, R.Z.,
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Luo, H.W.,
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ICWAPR16(25-30)
IEEE DOI
1611
Algorithm design and analysis
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Clustering nominal data using unsupervised binary decision trees:
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Elsevier DOI
1704
CUBT
BibRef
Yang, C.[Chao],
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Wang, J.L.[Jin-Liang],
Improving Land Use/Land Cover Classification by Integrating Pixel
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1802
BibRef
Cousty, J.[Jean],
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Hierarchical Segmentations with Graphs: Quasi-flat Zones, Minimum
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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
Turrisi da Costa, V.G.[Victor Guilherme],
Ponce de Leon Ferreira de Carvalho, A.C.[André Carlos],
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Strict Very Fast Decision Tree: A memory conservative algorithm for
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PRL(116), 2018, pp. 22-28.
Elsevier DOI
1812
Data stream mining, Machine learning, Memory-friendly algorithm
BibRef
Danda, S.[Sravan],
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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.],
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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],
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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],
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A parallel fuzzy rule-base based decision tree in the framework of
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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],
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Yu, W.Z.[Wei-Zhong],
Ren, F.[Fuji],
A linear multivariate binary decision tree classifier based on
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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
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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
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PR(146), 2024, pp. 110040.
Elsevier DOI
2311
Evidential decision tree, Dempster-Shafer theory,
Hierarchical interval estimation, Classification, Fractal-based belief entropy
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
BibRef
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).
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
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
BibRef
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
BibRef
Laptev, D.[Dmitry],
Buhmann, J.M.[Joachim M.],
Transformation-Invariant Convolutional Jungles,
CVPR15(3043-3051)
IEEE DOI
1510
BibRef
Earlier:
Convolutional Decision Trees for Feature Learning and Segmentation,
GCPR14(95-106).
Springer DOI
1411
Award, GCPR, HM.
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Ylioinas, J.[Juha],
Kannala, J.H.[Ju-Ho],
Hadid, A.[Abdenour],
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WACV14(347-354)
IEEE DOI
1406
Accuracy; Decision trees; Entropy; Geometry; Materials; Robustness; Training
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Xia, Y.[Yan],
He, K.M.[Kai-Ming],
Wen, F.[Fang],
Sun, J.[Jian],
Joint Inverted Indexing,
ICCV13(3416-3423)
IEEE DOI
1403
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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.],
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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
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MCPR14(51-60).
Springer DOI
1407
BibRef
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
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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
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CVPR13(843-850)
IEEE DOI
1309
image classification
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Zhong, C.M.[Cai-Ming],
Malinen, M.[Mikko],
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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],
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Szlam, A.[Arthur],
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Le Cun, Y.L.[Yann L.],
Fast Approximations to Structured Sparse Coding and Applications to
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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,
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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
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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).
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0912
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Patvardhan, C.,
Prakash, V.P.[V. Prem],
Novel Deterministic Heuristics for Building Minimum Spanning Trees with
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0912
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Yang, Y.L.[Ya-Ling],
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IEEE DOI
0910
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Li, W.L.[Wen-Long],
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Remote Sensing Image Extraction and Precision Analysis for Alpine
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CISP09(1-6).
IEEE DOI
0910
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Garcia-Gutierrez, J.[Jorge],
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CIARP11(355-362).
Springer DOI
1111
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de Sá, J.P.M.[J. P. Marques],
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CAIP09(799-807).
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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
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CVPR09(549-556).
IEEE DOI
0906
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Aziere, N.[Nicolas],
Todorovic, S.[Sinisa],
Ensemble Deep Manifold Similarity Learning Using Hard Proxies,
CVPR19(7291-7299).
IEEE DOI
2002
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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],
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Properties of the k-norm pruning algorithm for decision tree
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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
BibRef
Haynes, K.,
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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.],
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Learning to Detect Objects of Many Classes Using Binary Classifiers,
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Springer DOI
0608
Create a decision tree classifier, where each node is based on
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Gangaputra, S.[Sachin],
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IEEE DOI
0606
Nested representation of binary classifiers.
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Lee, S.T.[Seng-Tai],
Kim, J.[Jeehoon],
Baek, J.Y.[Jae-Yeon],
Han, M.W.[Man-Wi],
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Chon, T.S.[Tae-Soo],
Pattern Analysis of Movement Behavior of Medaka (Oryzias latipes)
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CAIP05(546).
Springer DOI
0509
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Random Forests Classification .