14.4.3.1 Random Forests Classification

Dietterich, T.G.,
Random Forests,
MachLearn(40), No. 2, 2000, pp. 139-157. BibRef 0001

Breiman, L.,
Random Forests,
MachLearn(45), No. 1, 2001, pp. 5-32.
DOI Link
See also Bagging Predictors. BibRef 0100

Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.,
Investigation of the Random Forest Framework for Classification of Hyperspectral Data,
GeoRS(43), No. 3, March 2005, pp. 492-501.
IEEE Abstract. 0501
BibRef

Kang, Y.[Yousun], Nagahashi, H.[Hiroshi], Sugimoto, A.[Akihiro],
Image Categorization Using Scene-Context Scale Based on Random Forests,
IEICE(E94-D), No. 9, September 2011, pp. 1809-1816.
WWW Link. 1110
BibRef
Earlier:
Semantic Segmentation and Object Recognition Using Scene-Context Scale,
PSIVT10(39-45).
IEEE DOI 1011
Effective region size to classify pixels. BibRef

Aoki, K.[Kota], Nagahashi, H.[Hiroshi],
Visual Correspondence Grouping via Local Consistent Neighborhood,
IEICE(E96-D), No. 6, June 2013, pp. 1351-1358.
WWW Link. 1306
Keypoint matches. BibRef

Ye, Y.M.[Yun-Ming], Wu, Q.Y.[Qing-Yao], Huang, J.Z.[Joshua Zhexue], Ng, M.K.[Michael K.], Li, X.T.[Xu-Tao],
Stratified sampling for feature subspace selection in random forests for high dimensional data,
PR(46), No. 3, March 2013, pp. 769-787.
Elsevier DOI 1212
Stratified sampling; High-dimensional data; Classification; Ensemble classifier; Decision trees; Random forests BibRef

Bosch, A.[Anna], Zisserman, A.[Andrew], Muñoz, X.[Xavier],
Scene Classification Using a Hybrid Generative/Discriminative Approach,
PAMI(30), No. 4, April 2008, pp. 712-727.
IEEE DOI 0803
BibRef
Earlier:
Image Classification using Random Forests and Ferns,
ICCV07(1-8).
IEEE DOI 0710
BibRef
Earlier:
Scene Classification Via pLSA,
ECCV06(IV: 517-530).
Springer DOI 0608
BibRef

Bosch, A.[Anna], Zisserman, A.[Andrew], Munoz, X.[Xavier],
Representing shape with a spatial pyramid kernel,
CIVR07(401-408).
DOI Link 0707
BibRef

Bosch, A.[Anna], Muñoz, X.[Xavier], Marti, J.,
Using Appearance and Context for Outdoor Scene Object Classification,
ICIP05(II: 1218-1221).
IEEE DOI 0512
BibRef

Bosch, A.[Anna], Munoz, X.[Xavier], Oliver, A.[Arnau], Marti, R.[Robert],
Object and Scene Classification: What does a Supervised Approach Provide us?,
ICPR06(I: 773-777).
IEEE DOI 0609
BibRef

Rota Bulò, S.[Samuel], Torsello, A.[Andrea], Pelillo, M.[Marcello],
A game-theoretic approach to partial clique enumeration,
IVC(27), No. 7, 4 June 2009, pp. 911-922.
Elsevier DOI 0904
BibRef
Earlier:
A Continuous-Based Approach for Partial Clique Enumeration,
GbRPR07(61-70).
Springer DOI 0706
Maximal clique enumeration; Maximum clique problem; Evolutionary game theory; Evolutionary stable strategy
See also Polynomial-Time Metrics for Attributed Trees. BibRef

Albarelli, A.[Andrea], Rota Bulo, S.[Samuel], Torsello, A.[Andrea], Pelillo, M.[Marcello],
Matching as a non-cooperative game,
ICCV09(1319-1326).
IEEE DOI 0909

See also Fast 3D surface reconstruction by unambiguous compound phase coding. BibRef

Rota Bulo, S., Albarelli, A., Torsello, A., Pelillo, M.,
A hypergraph-based approach to affine parameters estimation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Kontschieder, P.[Peter], Fiterau, M., Criminisi, A., Rota Bulò, S.[Samuel],
Deep Neural Decision Forests,
ICCV15(1467-1475)
IEEE DOI 1602
Award, Marr Prize. Computer vision BibRef

Kontschieder, P.[Peter], Rota Bulò, S.[Samuel], Donoser, M.[Michael], Pelillo, M.[Marcello], Bischof, H.[Horst],
Semantic Image Labelling as a Label Puzzle Game,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Genuer, R.[Robin], Poggi, J.M.[Jean-Michel], Tuleau-Malot, C.[Christine],
Variable selection using random forests,
PRL(31), No. 14, 15 October 2010, pp. 2225-2236.
Elsevier DOI 1003
Random forests; Regression; Classification; Variable importance; Variable selection; High dimensional data BibRef

Whalen, S.[Sean], Peisert, S.[Sean], Bishop, M.[Matt],
Multiclass classification of distributed memory parallel computations,
PRL(34), No. 3, 1 February 2013, pp. 322-329.
Elsevier DOI 1301
Multiclass classification; Bayesian networks; Random forests; Self-organizing maps; High performance computing; Communication patterns BibRef

Désir, C.[Chesner], Bernard, S.[Simon], Petitjean, C.[Caroline], Heutte, L.[Laurent],
One class random forests,
PR(46), No. 12, 2013, pp. 3490-3506.
Elsevier DOI 1308
BibRef
Earlier:
A Random Forest Based Approach for One Class Classification in Medical Imaging,
MLMI12(250-257).
Springer DOI 1211
BibRef
Earlier:
A New Random Forest Method for One-Class Classification,
SSSPR12(282-290).
Springer DOI 1211
One class classification BibRef

Zhang, L.[Le], Suganthan, P.N.[Ponnuthurai Nagaratnam],
Random Forests with ensemble of feature spaces,
PR(47), No. 10, 2014, pp. 3429-3437.
Elsevier DOI 1406
Ensemble BibRef

Hernández-Lobato, D.[Daniel], Martínez-Muñoz, G.[Gonzalo], Suárez, A.[Alberto],
How large should ensembles of classifiers be?,
PR(46), No. 5, May 2013, pp. 1323-1336.
Elsevier DOI 1302
Ensemble learning; Bagging; Random forest; Asymptotic ensemble prediction; Ensemble size BibRef

Quadrianto, N.[Novi], Ghahramani, Z.[Zoubin],
A Very Simple Safe-Bayesian Random Forest,
PAMI(37), No. 6, June 2015, pp. 1297-1303.
IEEE DOI 1506
Bayes methods BibRef

Du, P.J.[Pei-Jun], Samat, A.[Alim], Waske, B.[Björn], Liu, S.C.[Si-Cong], Li, Z.H.[Zhen-Hong],
Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features,
PandRS(105), No. 1, 2015, pp. 38-53.
Elsevier DOI 1506
Polarimetric SAR BibRef

Millard, K.[Koreen], Richardson, M.[Murray],
On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping,
RS(7), No. 7, 2015, pp. 8489.
DOI Link 1506
BibRef

Balzter, H.[Heiko], Cole, B.[Beth], Thiel, C.[Christian], Schmullius, C.[Christiane],
Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests,
RS(7), No. 11, 2015, pp. 14876.
DOI Link 1512
BibRef

Liu, X.[Xiao], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng], Liu, Z.C.[Zi-Cheng], Zhang, L.M.[Lu-Ming], Chen, C.[Chun], Bu, J.J.[Jia-Jun],
Random Forest Construction With Robust Semisupervised Node Splitting,
IP(24), No. 1, January 2015, pp. 471-483.
IEEE DOI 1502
BibRef
Earlier:
Semi-supervised Node Splitting for Random Forest Construction,
CVPR13(492-499)
IEEE DOI 1309
node splitting; random forest; semi-supervised learning
See also Attribute-restricted latent topic model for person re-identification. BibRef

Belgiu, M.[Mariana], Dragut, L.[Lucian],
Random forest in remote sensing: A review of applications and future directions,
PandRS(114), No. 1, 2016, pp. 24-31.
Elsevier DOI 1604
Survey, Random Forests. Random forest BibRef

Xia, J.[Jing], Zhang, S.Y.[Sheng-Yu], Cai, G.L.[Guo-Long], Li, L.[Li], Pan, Q.[Qing], Yan, J.[Jing], Ning, G.M.[Gang-Min],
Adjusted weight voting algorithm for random forests in handling missing values,
PR(69), No. 1, 2017, pp. 52-60.
Elsevier DOI 1706
Random forests BibRef

Tsagkrasoulis, D.[Dimosthenis], Montana, G.[Giovanni],
Random forest regression for manifold-valued responses,
PRL(101), No. 1, 2018, pp. 6-13.
Elsevier DOI 1801
Manifold regression BibRef

Jampour, M.[Mahdi], Moin, M.S.[Mohammad-Shahram], Bischof, L.F.Y.H.[Lap-Fai Yu Horst],
Mapping Forests: A Comprehensive Approach for Nonlinear Mapping Problems,
JMIV(60), No. 2, February 2018, pp. 232-245.
Springer DOI 1802
BibRef

Jinguji, A.[Akira], Sato, S.[Shimpei], Nakahara, H.[Hiroki],
An FPGA Realization of a Random Forest with k-Means Clustering Using a High-Level Synthesis Design,
IEICE(E101-D), No. 2, February 2018, pp. 354-362.
WWW Link. 1802
BibRef

Paul, A., Mukherjee, D.P., Das, P., Gangopadhyay, A., Chintha, A.R., Kundu, S.,
Improved Random Forest for Classification,
IP(27), No. 8, August 2018, pp. 4012-4024.
IEEE DOI 1806
Etching, Feature extraction, Ferrites, Manuals, Microstructure, Steel, Vegetation, Random forest, classification accuracy, optimal number of trees BibRef

Paul, A.[Angshuman], Mukherjee, D.P.[Dipti Prasad],
Reinforced quasi-random forest,
PR(94), 2019, pp. 13-24.
Elsevier DOI 1906
Random forest, Reinforcement learning, Orthogonal decision trees, Importance of attributes BibRef

O'Brien, R.[Robert], Ishwaran, H.[Hemant],
A random forests quantile classifier for class imbalanced data,
PR(90), 2019, pp. 232-249.
Elsevier DOI 1903
Weighted Bayes classifier, Response-based sampling, Class imbalance, Minority class, Random forests BibRef

Zafari, A.[Azar], Zurita-Milla, R.[Raul], Izquierdo-Verdiguier, E.[Emma],
Evaluating the Performance of a Random Forest Kernel for Land Cover Classification,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef
And: Correction: RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Andrejchenko, V.[Vera], Liao, W.Z.[Wen-Zhi], Philips, W.[Wilfried], Scheunders, P.[Paul],
Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Kang, B., Nguyen, T.Q.,
Random Forest With Learned Representations for Semantic Segmentation,
IP(28), No. 7, July 2019, pp. 3542-3555.
IEEE DOI 1906
Semantics, Shape, Image segmentation, Convolution, Task analysis, Neural networks, Real-time systems, Semantic segmentation, real-time systems BibRef

Gazzola, G.[Gianluca], Jeong, M.K.[Myong Kee],
Dependence-biased clustering for variable selection with random forests,
PR(96), 2019, pp. 106980.
Elsevier DOI 1909
Variable selection, Random forest, Permutation importance, Regression, Classification, Clustering BibRef

Maas, A.E.[Alina E.], Rottensteiner, F.[Franz], Heipke, C.[Christian],
A label noise tolerant random forest for the classification of remote sensing data based on outdated maps for training,
CVIU(188), 2019, pp. 102782.
Elsevier DOI 1910
Change detection, Label noise, Random forest, Supervised classification BibRef

Katuwal, R.[Rakesh], Suganthan, P.N., Zhang, L.[Le],
Heterogeneous oblique random forest,
PR(99), 2020, pp. 107078.
Elsevier DOI 1912
Benchmarking, Classifiers, Oblique random forest, Heterogeneous, One-vs-all, Ensemble learning BibRef

Zhang, Y.Q.[You-Qiang], Cao, G.[Guo], Li, X.S.[Xue-Song], Wang, B.S.[Bi-Sheng], Fu, P.[Peng],
Active Semi-Supervised Random Forest for Hyperspectral Image Classification,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Tahir, M.[Muhammad], Taj, I.A.[Imtiaz A.], Assuncao, P.A.[Pedro A.], Asif, M.[Muhammad],
Fast video encoding based on random forests,
RealTimeIP(17), No. 4, August 2020, pp. 1029-1049.
Springer DOI 2007
BibRef

Vitrack-Tamam, S.[Snir], Holtzman, L.[Lilach], Dagan, R.[Reut], Levi, S.[Shai], Tadmor, Y.[Yuval], Azizi, T.[Tamir], Rabinovitz, O.[Onn], Naor, A.[Amos], Liran, O.[Oded],
Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Phan, T.N.[Thanh Noi], Kuch, V.[Verena], Lehnert, L.W.[Lukas W.],
Land Cover Classification using Google Earth Engine and Random Forest Classifier: The Role of Image Composition,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Obata, S.[Shingo], Cieszewski, C.J.[Chris J.], Lowe, R.C.[Roger C.], Bettinger, P.[Pete],
Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense Landsat Time Series and FIA Dataset,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Marques F., P.C.[Paulo C.],
Confidence intervals for the random forest generalization error,
PRL(158), 2022, pp. 171-175.
Elsevier DOI 2205

WWW Link. Code, Random Forest. Random forests, Generalization error, Out-of-bag estimation, Confidence interval, Bootstrapping BibRef

Boston, T.[Tony], van Dijk, A.[Albert], Larraondo, P.R.[Pablo Rozas], Thackway, R.[Richard],
Comparing CNNs and Random Forests for Landsat Image Segmentation Trained on a Large Proxy Land Cover Dataset,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Jozwicki, D.[Dorota], Sharma, P.[Puneet], Mann, I.[Ingrid], Hoppe, U.P.[Ulf-Peter],
Segmentation of PMSE Data Using Random Forests,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Bicego, M.[Manuele],
DisRFC: a dissimilarity-based Random Forest Clustering approach,
PR(133), 2023, pp. 109036.
Elsevier DOI 2210
Random forests clustering, Dissimilarities, Unsupervised learning, Clustering, Non-vectorial representation BibRef

Schulthess, U.[Urs], Rodrigues, F.[Francelino], Taymans, M.[Matthieu], Bellemans, N.[Nicolas], Bontemps, S.[Sophie], Ortiz-Monasterio, I.[Ivan], Gérard, B.[Bruno], Defourny, P.[Pierre],
Optimal Sample Size and Composition for Crop Classification with Sen2-Agri's Random Forest Classifier,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Almeida, R.[Raquel], Kijak, E.[Ewa], Malinowski, S.[Simon], Patrocínio, Jr., Z.K.G.[Zenilton K.G.], Araújo, A.A.[Arnaldo A.], Guimarães, S.J.F.[Silvio J.F.],
Graph-based image gradients aggregated with random forests,
PRL(166), 2023, pp. 182-189.
Elsevier DOI 2302
Random forest, Graph, Segmentation, Edge detection, Hierarchical watershed BibRef

Mensi, A.[Antonella], Tax, D.M.J.[David M.J.], Bicego, M.[Manuele],
Detecting outliers from pairwise proximities: Proximity isolation forests,
PR(138), 2023, pp. 109334.
Elsevier DOI 2303
Random forest, Outlier detection, Isolation, Pairwise distances BibRef

Rhodes, J.S.[Jake S.], Cutler, A.[Adele], Moon, K.R.[Kevin R.],
Geometry- and Accuracy-Preserving Random Forest Proximities,
PAMI(45), No. 9, September 2023, pp. 10947-10959.
IEEE DOI 2309
BibRef

Ding, Y.[Yan], Zhai, Y.[Yujuan], Hu, M.[Ming], Zhao, J.[Jia],
Deep forest auto-Encoder for resource-Centric attributes graph embedding,
PR(143), 2023, pp. 109747.
Elsevier DOI 2310
Graph embedding, Deep random forest, Deep auto-encoder, Self-attention BibRef

Ji, J.Z.[Jun-Zhong], Li, J.W.[Jun-Wei],
Tri-objective optimization-based cascade ensemble pruning for deep forest,
PR(143), 2023, pp. 109744.
Elsevier DOI 2310
Ensemble learning, Ensemble pruning, Deep forest, Multi-objective optimization, Coupled diversity BibRef

Jiang, M.[Mudi], Wang, J.Q.[Jia-Qi], Hu, L.[Lianyu], He, Z.[Zengyou],
Random forest clustering for discrete sequences,
PRL(174), 2023, pp. 145-151.
Elsevier DOI 2310
Clustering, Random forest, Sequential data, Decision trees BibRef

Yuan, X.G.[Xiao-Guang], Liu, S.[Shiruo], Feng, W.[Wei], Dauphin, G.[Gabriel],
Feature Importance Ranking of Random Forest-Based End-to-End Learning Algorithm,
RS(15), No. 21, 2023, pp. 5203.
DOI Link 2311
BibRef

Galka, L.[Lukasz], Karczmarek, P.[Pawel],
Deterministic attribute selection for isolation forest,
PR(151), 2024, pp. 110395.
Elsevier DOI 2404
Anomaly detection, Isolation forest, Outlier detection, Deterministic model BibRef

Raniero, M.[Matteo], Bicego, M.[Manuele], Cicalese, F.[Ferdinando],
Distance-Based Random Forest Clustering with Missing Data,
CIAP22(III:121-132).
Springer DOI 2205
BibRef

Bicego, M.[Manuele], Escolano, F.[Francisco],
On learning Random Forests for Random Forest-clustering,
ICPR21(3451-3458)
IEEE DOI 2105
Radio frequency, Estimation, Entropy, Random forests, Guidelines BibRef

Mensi, A.[Antonella], Bicego, M.[Manuele], Tax, D.M.J.[David M.J.],
Proximity Isolation Forests,
ICPR21(8021-8028)
IEEE DOI 2105
Prototypes, Vegetation, Feature extraction, Task analysis, Anomaly detection, Optimization BibRef

Tombe, R.[Ronald], Viriri, S.[Serestina], Dombeu, J.V.F.[Jean Vincent Fonou],
Scene Classification of Remote Sensing Images Based on Convnet Features and Multi-Grained Forest,
ISVC20(I:731-740).
Springer DOI 2103
BibRef

Gupta, K., Awate, S.P.,
Random Forests for Simultaneous-Multislice (SMS) Undersampled HARDI Reconstruction and Uncertainty Estimation,
ICIP19(2626-2630)
IEEE DOI 1910
HARDI, simultaneous multislice MRI, reconstruction, random forest, uncertainty BibRef

Mensi, A.[Antonella], Bicego, M.[Manuele],
A Novel Anomaly Score for Isolation Forests,
CIAP19(I:152-163).
Springer DOI 1909
BibRef

Zhou, X., Ding, P.L.K., Li, B.,
Improving Robustness of Random Forest Under Label Noise,
WACV19(950-958)
IEEE DOI 1904
learning (artificial intelligence), pattern classification, global multiclass noise, classic random forest model, Training data BibRef

Qiu, Q.A.[Qi-Ang], Lezama, J.[José], Bronstein, A.M.[Alex M.], Sapiro, G.[Guillermo],
ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks,
ECCV18(II: 442-459).
Springer DOI 1810
BibRef

Tewari, A., Gu, F., Grandidier, F., Stricker, D.,
Quantitative Evaluation of Salient Deep Neural Network Features Using Random Forests,
IVMSP18(1-5)
IEEE DOI 1809
Task analysis, Training, Artificial neural networks, Vegetation, Feature extraction, Shape, CNN, Feature Evaluation, Random Forests BibRef

Burciu, I.[Irina], Martinetz, T.[Thomas], Barth, E.[Erhardt],
Sensing Forest for Pattern Recognition,
ACIVS17(126-137).
Springer DOI 1712
Prototype based random forest. BibRef

Kiran, B.R.[B. Ravi], Serra, J.[Jean],
Cost-Complexity Pruning of Random Forests,
ISMM17(222-232).
Springer DOI 1706
BibRef

Tran, A.[Antoine], Manzanera, A.[Antoine],
Fast growing hough forest as a stable model for object detection,
IPTA16(1-6)
IEEE DOI 1703
Hough transforms BibRef

Wallenberg, M.[Marcus], Forssén, P.E.[Per-Erik],
Improving Random Forests by Correlation-Enhancing Projections and Sample-Based Sparse Discriminant Selection,
CRV16(222-227)
IEEE DOI 1612
image classification BibRef

Moradi, M.[Mehdi], Syeda-Mahmood, T.[Tanveer], Hor, S.[Soheil],
Tree-Based Transforms for Privileged Learning,
MLMI16(188-195).
Springer DOI 1611
BibRef

Naji, D., Fakir, M., Bouikhalene, B., Elayachi, R.,
Recognition of 3D Objects Using Heat Diffusion Equations and Random Forests,
CGiV16(161-166)
IEEE DOI 1608
decision trees BibRef

Rodriguez, A.L.[Antonio L.], Sequeira, V.[Vitor],
Improving Ferns Ensembles by Sparsifying and Quantising Posterior Probabilities,
ICCV15(4103-4111)
IEEE DOI 1602
Image recognition BibRef

Modolo, D.[Davide], Vezhnevets, A.[Alexander], Ferrari, V.[Vittorio],
Context Forest for Object Class Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

de Vleeschouwer, C., Legrand, A., Jacques, L., Hebert, M.[Martial],
Mitigating memory requirements for random trees/ferns,
ICIP15(227-231)
IEEE DOI 1512
Random ferns; memory usage; random trees BibRef

Chen, F.[Fan], Liu, Z.C.[Zi-Cheng], Sun, M.T.[Ming-Ting],
Anomaly detection by using random projection forest,
ICIP15(1210-1214)
IEEE DOI 1512
Anomaly Detection; Intelligent Video Surveillance; Random Forest BibRef

Ren, S.Q.[Shao-Qing], Cao, X.D.[Xu-Dong], Wei, Y.C.[Yi-Chen], Sun, J.[Jian],
Global refinement of random forest,
CVPR15(723-730)
IEEE DOI 1510
BibRef

Baumann, F.[Florian], Vogt, K.[Karsten], Ehlers, A.[Arne], Rosenhahn, B.[Bodo],
Probabilistic nodes for modelling classification uncertainty for random forest,
MVA15(510-513)
IEEE DOI 1507
Decision trees BibRef

Villamizar, M., Garrell, A., Sanfeliu, A., Moreno-Noguer, F.,
Multimodal Object Recognition Using Random Clustering Trees,
IbPRIA15(496-504).
Springer DOI 1506
BibRef

Welbl, J.[Johannes],
Casting Random Forests as Artificial Neural Networks (and Profiting from It),
GCPR14(765-771).
Springer DOI 1411
BibRef

Haeusler, R.[Ralf], Nair, R.[Rahul], Kondermann, D.[Daniel],
Ensemble Learning for Confidence Measures in Stereo Vision,
CVPR13(305-312)
IEEE DOI 1309
benchmarking; confidence measure; random forest; stereo BibRef

Wohlhart, P.[Paul], Kostinger, M.[Martin], Donoser, M.[Michael], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Optimizing 1-Nearest Prototype Classifiers,
CVPR13(460-467)
IEEE DOI 1309
Classification BibRef

Schulter, S.[Samuel], Leistner, C.[Christian], Bischof, H.[Horst],
Fast and accurate image upscaling with super-resolution forests,
CVPR15(3791-3799)
IEEE DOI 1510
BibRef

Schulter, S.[Samuel], Leistner, C.[Christian], Wohlhart, P.[Paul], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Accurate Object Detection with Joint Classification-Regression Random Forests,
CVPR14(923-930)
IEEE DOI 1409
BibRef

Wohlhart, P.[Paul], Lepetit, V.[Vincent],
Learning descriptors for object recognition and 3D pose estimation,
CVPR15(3109-3118)
IEEE DOI 1510
BibRef

Schulter, S.[Samuel], Leistner, C.[Christian], Wohlhart, P.[Paul], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Alternating Regression Forests for Object Detection and Pose Estimation,
ICCV13(417-424)
IEEE DOI 1403
Head Pose Estimation; Object Detection; Random Forest; Regression
See also On-line Hough Forests. BibRef

Schulter, S.[Samuel], Wohlhart, P.[Paul], Leistner, C.[Christian], Saffari, A.[Amir], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Alternating Decision Forests,
CVPR13(508-515)
IEEE DOI 1309
Boosting; Global Loss; Random Forests BibRef

Straehle, C.[Christoph], Kandemir, M.[Melih], Koethe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Multiple Instance Learning with Response-Optimized Random Forests,
ICPR14(3768-3773)
IEEE DOI 1412
Accuracy BibRef

Straehle, C.[Christoph], Koethe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria,
ICCV13(1849-1856)
IEEE DOI 1403
decision tree BibRef

Straehle, C.[Christoph], Peter, S.[Sven], Köthe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
K-Smallest Spanning Tree Segmentations,
GCPR13(375-384).
Springer DOI 1311
Award, GCPR, HM. BibRef

Baumann, F.[Florian], Ehlers, A.[Arne], Rosenhahn, B.[Bodo], Liu, W.[Wei],
Sequential Boosting for Learning a Random Forest Classifier,
WACV15(442-447)
IEEE DOI 1503
Accuracy BibRef

Baumann, F.[Florian], Li, F.D.[Fang-Da], Ehlers, A.[Arne], Rosenhahn, B.[Bodo],
Thresholding a Random Forest Classifier,
ISVC14(II: 95-106).
Springer DOI 1501
BibRef

Baumann, F.[Florian], Chen, J.H.[Jing-Hui], Vogt, K.[Karsten], Rosenhahn, B.[Bodo],
Improved Threshold Selection by Using Calibrated Probabilities for Random Forest Classifiers,
CRV15(155-160)
IEEE DOI 1507
Accuracy BibRef

Baumann, F.[Florian], Ehlers, A.[Arne], Vogt, K.[Karsten], Rosenhahn, B.[Bodo],
Cascaded Random Forest for Fast Object Detection,
SCIA13(131-142).
Springer DOI 1311
BibRef

Fu, H.[Hao], Zhang, Q.[Qian], Qiu, G.P.[Guo-Ping],
Random Forest for Image Annotation,
ECCV12(VI: 86-99).
Springer DOI 1210
BibRef

Nowozin, S.[Sebastian], Rother, C.[Carsten], Bagon, S.[Shai], Sharp, T.[Toby], Yao, B.P.[Bang-Peng], Kohli, P.[Pushmeet],
Decision tree fields,
ICCV11(1668-1675).
IEEE DOI 1201
Combines and generalizes random forests and conditional random fields. BibRef

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ACCV09(III: 621-632).
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CVPR11(1377-1384).
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Food-101: Mining Discriminative Components with Random Forests,
ECCV14(VI: 446-461).
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Dataset, Food. 101 food categories, with 101’000 images recognizing pictured dishes. BibRef

Hoo, W.L.[Wai Lam], Kim, T.K.[Tae-Kyun], Pei, Y.[Yuru], Chan, C.S.[Chee Seng],
Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,
ICPR14(3434-3439)
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Accuracy BibRef

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Building Emerging Pattern (EP) Random forest for recognition,
ICIP10(1457-1460).
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Wang, A.P.[Ai-Ping], Wan, G.W.[Guo-Wei], Cheng, Z.Q.[Zhi-Quan], Li, S.K.[Si-Kun],
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Random Forest Clustering and Application to Video Segmentation,
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Large data sets, high-dimensional space. BibRef

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Online incremental random forests,
ICMV07(102-106).
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Feature selection technique. BibRef

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Online random forests based on CorrFS and CorrBE,
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IEEE DOI 0806
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Prior, M., Windeatt, T.,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Self-Organizing Map Classification .