awesome-random-forest
Forest toolkit
A curated collection of resources and tools for the Random Forest algorithm
Random Forest - a curated list of resources regarding random forest
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last commit: almost 3 years agoLinked from 3 awesome lists
Awesome Random Forest / Codes / Matlab | |||
| http://vision.ucsd.edu/~pdollar/toolbox/doc/ | [Piotr Dollar's toolbox] ( ) | ||
| https://github.com/karpathy/Random-Forest-Matlab | 183 | over 12 years ago | [Andrej Karpathy's toolbox] ( ) |
| http://www.cs.rtu.lv/jekabsons/regression.html | [M5PrimeLab by Gints Jekabsons] ( ) | ||
Awesome Random Forest / Codes / R | |||
| http://cran.r-project.org/web/packages/randomForest/ | [Breiman and Cutler's random forests] ( ) | ||
| Hothorn et al.'s party package with cforest function | |||
Awesome Random Forest / Codes / C/C++ | |||
| http://research.microsoft.com/en-us/downloads/52d5b9c3-a638-42a1-94a5-d549e2251728/ | [Sherwood library] ( ) | ||
| http://www.montefiore.ulg.ac.be/~geurts/Software.html | [Regression tree package by Pierre Geurts] ( ) | ||
| https://github.com/imbs-hl/ranger | 780 | almost 2 years ago | [ranger: A Fast Implementation of Random Forests] ( ) |
Awesome Random Forest / Codes / Python | |||
| http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble | [Scikit-learn] ( ) | ||
Awesome Random Forest / Codes / JavaScript | |||
| https://github.com/karpathy/forestjs | 300 | almost 14 years ago | [Forestjs] ( ) |
Awesome Random Forest / Codes / Go (golang) | |||
| https://github.com/ryanbressler/CloudForest | 740 | over 4 years ago | [CloudForest] ( ) |
Awesome Random Forest / Theory / Lectures | |||
| http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/ | [ICCV 2013 Tutorial : Decision Forests and Fields for Computer Vision] ( ) by Jamie Shotton and Sebastian Nowozin | ||
Awesome Random Forest / Theory / Lectures / http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/ | |||
| http://techtalks.tv/talks/randomized-decision-forests-and-their-applications-in-computer-vision-jamie/59432/ | [Lecture 1] ( ) : Randomized Decision Forests and their Applications in Computer Vision I (Decision Forest, Classification Forest, | ||
| http://techtalks.tv/talks/decision-jungles-jamie-second-half-of-above/59434/ | [Lecture 2] ( ) : Randomized Decision Forests and their Applications in Computer Vision II (Regression Forest, Decision Jungle) | ||
| http://techtalks.tv/talks/entropy-estimation-and-streaming-data-sebastian/59433/ | [Lecture 3] ( ) : Entropy estimation and streaming data | ||
| http://techtalks.tv/talks/decision-and-regression-tree-fields-sebastian/59435/ | [Lecture 4] ( ) : Decision and Regression Tree Fields | ||
Awesome Random Forest / Theory / Lectures | |||
| http://www.cs.ubc.ca/~nando/540-2013/lectures.html | [UBC Machine Learning] ( ) by Nando de Freitas | ||
Awesome Random Forest / Theory / Lectures / http://www.cs.ubc.ca/~nando/540-2013/lectures.html | |||
| http://www.cs.ubc.ca/~nando/540-2013/lectures/l8.pdf | [Lecture 8 slide] ( ) , [Lecture 8 video] ( ) : Decision trees | ||
| http://www.cs.ubc.ca/~nando/540-2013/lectures/l9.pdf | [Lecture 9 slide] ( ) , [Lecture 9 video] ( ) : Random forests | ||
| https://www.youtube.com/watch?v=zFGPjRPwyFw&index=13&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6 | [Lecture 10 video] ( ) : Random forest applications | ||
Awesome Random Forest / Theory / Books / Antonio Criminisi, Jamie Shotton (2013) | |||
| http://link.springer.com/book/10.1007%2F978-1-4471-4929-3 | [Decision Forests for Computer Vision and Medical Image Analysis] ( ) | ||
Awesome Random Forest / Theory / Books / Trevor Hastie, Robert Tibshirani, Jerome Friedman (2008) | |||
| http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf | [The Elements of Statistical Learning, (Chapter 10, 15, and 16)] ( ) | ||
Awesome Random Forest / Theory / Books / Luc Devroye, Laszlo Gyorfi, Gabor Lugosi (1996) | |||
| A Probabilistic Theory of Pattern Recognition (Chapter 20, 21) | |||
Awesome Random Forest / Theory / Papers | |||
| [Paper] | Consistency of random forests | ||
| [Paper] | On the asymptotics of random forests | ||
| http://jmlr.org/proceedings/papers/v32/denil14.pdf)] | Random Forests In Theory and In Practice [[Paper] ( | ||
| Paper | Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease [ ] | ||
| Paper | Deep Neural Decision Forests [ ] | ||
| Paper | Canonical Correlation Forests [ ] | ||
| http://arxiv.org/pdf/1507.07583.pdf)] | Relating Cascaded Random Forests to Deep Convolutional Neural Networks [[Paper] ( | ||
| http://jmlr.org/proceedings/papers/v37/matthew15.pdf)] | Bayesian Forests [[Paper] ( | ||
| [Paper] | Mondrian Forests: Efficient Online Random Forests | ||
| Paper | Extremely randomized trees P Geurts, D Ernst, L Wehenkel - Machine learning, 2006 [ ] [[Code] ( | ||
| http://research.microsoft.com/pubs/205439/DecisionJunglesNIPS2013.pdf)] | Decision Jungles [[Paper] ( | ||
Awesome Random Forest / Theory / Papers / http://research.microsoft.com/pubs/205439/DecisionJunglesNIPS2013.pdf)] | |||
| Paper | Laptev, Dmitry, and Joachim M. Buhmann. Transformation-invariant convolutional jungles. CVPR 2015. [ ] | ||
Awesome Random Forest / Theory / Papers | |||
| http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf)] | Semi-supervised Node Splitting for Random Forest Construction [[Paper] ( | ||
| http://www.nowozin.net/sebastian/papers/nowozin2012infogain.pdf)] | Improved Information Gain Estimates for Decision Tree Induction [[Paper] ( | ||
| http://lrs.icg.tugraz.at/pubs/leistner_eccv_10.pdf)] | MIForests: Multiple-Instance Learning with Randomized Trees [[Paper] ( [[Code] ( | ||
| Paper | Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari, Peter M. Roth, Horst Bischof: Alternating Decision Forests. CVPR 2013 | ||
| Paper | Decision Forests, Convolutional Networks and the Models in-Between [ ] | ||
| Paper | Random Uniform Forests Saïp Ciss [ ] [ ] | ||
| Paper | Autoencoder Trees, Ozan İrsoy, Ethem Alpaydın 2015 [ ] | ||
Awesome Random Forest / Thesis | |||
| [Repository] | 525 | about 10 years ago | with thesis and related codes |
Awesome Random Forest / Applications / Image classification | |||
| http://www.iai.uni-bonn.de/~gall/download/jgall_coarse2fine_cvpr15.pdf)] | ETH Zurich [[Paper-CVPR15] ( [[Paper-CVPR14] ( [[Paper-ECCV] ( | ||
| http://www.cs.huji.ac.il/~daphna/course/CoursePapers/bosch07a.pdf)] | University of Girona & University of Oxford [[Paper] ( | ||
Awesome Random Forest / Applications / Object Detection | |||
| http://lrs.icg.tugraz.at/pubs/schulter_cvpr_14.pdf)] | Graz University of Technology [[Paper-CVPR] ( [[Paper-ICCV] ( | ||
| http://www.iai.uni-bonn.de/~gall/download/jgall_houghforest_cvpr09.pdf)] | ETH Zurich + Microsoft Research Cambridge [[Paper] ( | ||
Awesome Random Forest / Applications / Object Tracking | |||
| http://campar.in.tum.de/pub/tanda2014cvpr/tanda2014cvpr.pdf)] | Technische Universitat Munchen [[Paper] ( | ||
| http://www.igp.ethz.ch/photogrammetry/publications/pdf_folder/LeaFenKuzRosSavCVPR14.pdf)] | ETH Zurich + Leibniz University Hannover + Stanford University [[Paper] ( | ||
| https://lrs.icg.tugraz.at/pubs/godec_iccv_11.pdf)] | Graz University of Technology [[Paper] ( | ||
Awesome Random Forest / Applications / Edge Detection | |||
| http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hallman_Oriented_Edge_Forests_2015_CVPR_paper.pdf)] | University of California, Irvine [[Paper] ( [[Code] ( | ||
| http://research-srv.microsoft.com/pubs/202540/DollarICCV13edges.pdf)] | Microsoft Research [[Paper] ( [[Code] ( | ||
| http://research.microsoft.com/en-us/um/people/larryz/cvpr13sketchtokens.pdf)] | Massachusetts Inst. of Technology + Microsoft Research [[Paper] ( [[Code] ( | ||
Awesome Random Forest / Applications / Semantic Segmentation | |||
| http://www.dsi.unive.it/~srotabul/files/publications/CVPR2014a.pdf)] | Fondazione Bruno Kessler, Microsoft Research Cambridge [[Paper] ( | ||
| http://step.polymtl.ca/~rv101/MICCAI-Laplacian-Forest.pdf)] | INRIA + Microsoft Research Cambridge [[Paper] ( | ||
| http://research.microsoft.com/pubs/146430/criminisi_ipmi_2011c.pdf)] | Microsoft Research Cambridge + GE Global Research Center + University of California + Rutgers Univeristy [[Paper] ( | ||
| http://mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-CVPR-semantic-texton-forests.pdf)] | University of Cambridge + Toshiba Corporate R&D Center [[Paper] ( | ||
Awesome Random Forest / Applications / Human / Hand Pose Estimation | |||
| http://research.microsoft.com/pubs/238453/pn362-sharp.pdf)][[Video-CHI] | Microsoft Research Cambridge [[Paper-CHI] ( ( [[Paper-CVPR] ( | ||
| http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Krupka_Discriminative_Ferns_Ensemble_2014_CVPR_paper.pdf)] | Microsoft Research Haifa [[Paper] ( | ||
| http://research.microsoft.com/en-us/people/yichenw/cvpr14_facealignment.pdf)] | Microsoft Research Asia [[Paper] ( | ||
| http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_xiaowei.pdf)] | Imperial College London [[Paper-CVPR-Face] ( [[Paper-CVPR-Hand] ( [[Paper-ICCV] ( | ||
| https://lirias.kuleuven.be/bitstream/123456789/398648/2/3601_open+access.pdf)] | ETH Zurich + Microsoft [[Paper] ( | ||
Awesome Random Forest / Applications / 3D localization | |||
| http://www.iis.ee.ic.ac.uk/icvl/doc/ECCV2014_aly.pdf)] | Imperial College London [[Paper] ( | ||
| http://abnerguzman.com/publications/gkgssfi_cvpr14.pdf)] | Microsoft Research Cambridge + University of Illinois + Imperial College London [[Paper] ( | ||
| http://research.microsoft.com/pubs/184826/relocforests.pdf)] | Microsoft Research Cambridge [[Paper] ( | ||
Awesome Random Forest / Applications / Low-Level vision / Super-Resolution | |||
| Paper | Technicolor R&I Hannover [ ] | ||
| http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schulter_Fast_and_Accurate_2015_CVPR_paper.pdf)] | Graz University of Technology [[Paper] ( | ||
Awesome Random Forest / Applications / Low-Level vision / Denoising | |||
| http://research.microsoft.com/pubs/217099/CVPR2014ForestFiltering.pdf)] | Microsoft Research + iCub Facility - Istituto Italiano di Tecnologia [[Paper] ( | ||
Awesome Random Forest / Applications / Facial expression recognition | |||
| Paper | Sorbonne Universites [ ] | ||
Awesome Random Forest / Applications / Interpretability, regularization, compression pruning and feature selection | |||
| http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ren_Global_Refinement_of_2015_CVPR_paper.pdf)] | Global Refinement of Random Forest [[Paper] ( | ||
| Paper | L1-based compression of random forest models Arnaud Joly, Fran¸cois Schnitzler, Pierre Geurts and Louis Wehenkel ESANN 2012 [ ] | ||
| http://jmlr.org/proceedings/papers/v37/nan15.pdf)] | Feature-Budgeted Random Forest [[Paper] ( [ ] | ||
Awesome Random Forest / Applications / Interpretability, regularization, compression pruning and feature selection / http://jmlr.org/proceedings/papers/v37/nan15.pdf)] | |||
| Paper | Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama NIPS 2016 [ ] | ||
Awesome Random Forest / Applications / Interpretability, regularization, compression pruning and feature selection | |||
| Paper | Meinshausen, Nicolai. "Node harvest." The Annals of Applied Statistics 4.4 (2010): 2049-2072. [ ] [ ] [ ] | ||
| Paper | Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach S. Hara, K. Hayashi, [ ] [ ] | ||
| Paper | Cui, Zhicheng, et al. "Optimal action extraction for random forests and boosted trees." ACM SIGKDD 2015. [ ] | ||
| Paper | DART: Dropouts meet Multiple Additive Regression Trees K. V. Rashmi, Ran Gilad-Bachrach [ ] | ||
| Paper | Begon, Jean-Michel, Arnaud Joly, and Pierre Geurts. Joint learning and pruning of decision forests. (2016). [ ] | ||
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