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Random Forest - a curated list of resources regarding tree-based methods and more, including but not limited to random forest, bagging and boosting.

Contributing

Please feel free to pull requests.

The project is not actively maintained.

Join the chat at https://gitter.im/kjw0612/awesome-random-forest

randomforest

Table of Contents

  • [Codes] (#codes)
  • Theory
    • Lectures
    • Books
    • [Papers] (#papers)
      • [Analysis / Understanding] (#analysis--understanding)
      • [Model variants] (#model-variants)
    • [Thesis] (#thesis)
  • [Applications] (#applications)
    • [Image Classification] (#image-classification)
    • [Object Detection] (#object-detection)
    • [Object Tracking] (#object-tracking)
    • [Edge Detection] (#edge-detection)
    • [Semantic Segmentation] (#semantic-segmentation)
    • [Human / Hand Pose Estimation] (#human--hand-pose-estimation)
    • [3D Localization] (#3d-localization)
    • [Low-Level Vision] (#low-level-vision)
    • [Facial Expression Recognition] (#facial-expression-recognition)
    • [Interpretability, regularization, compression pruning and feature selection](#Interpretability, regularization, compression pruning and feature selection)

Codes

Theory

Lectures

Books

Papers

Analysis / Understanding

  • Consistency of random forests [Paper]
  • Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests, The Annals of Statistics, in press.
  • On the asymptotics of random forests [Paper]
  • Scornet, E. (2015). On the asymptotics of random forests, Journal of Multivariate Analysis, in press.
  • Random Forests In Theory and In Practice [[Paper] (http://jmlr.org/proceedings/papers/v32/denil14.pdf)]
    • Misha Denil, David Matheson, Nando de Freitas, Narrowing the Gap: Random Forests In Theory and In Practice, ICML 2014
  • Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease [Paper]

Model variants

Thesis

  • Understanding Random Forests
  • PhD dissertation, Gilles Louppe, July 2014. Defended on October 9, 2014.
  • [Repository] with thesis and related codes

Applications

Image classification

Object Detection

Object Tracking

Edge Detection

Semantic Segmentation

Human / Hand Pose Estimation

3D localization

  • Imperial College London [[Paper] (http://www.iis.ee.ic.ac.uk/icvl/doc/ECCV2014_aly.pdf)]
    • Alykhan Tejani, Danhang Tang, Rigas Kouskouridas, and Tae-Kyun Kim, Latent-Class Hough Forests for 3D Object Detection and Pose Estimation, ECCV 2014
  • Microsoft Research Cambridge + University of Illinois + Imperial College London [[Paper] (http://abnerguzman.com/publications/gkgssfi_cvpr14.pdf)]
    • Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, and Shahram Izadi, Multi-Output Learning for Camera Relocalization, CVPR 2014
  • Microsoft Research Cambridge [[Paper] (http://research.microsoft.com/pubs/184826/relocforests.pdf)]
    • Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon, Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images, CVPR 2013

Low-Level vision

Facial expression recognition

  • Sorbonne Universites [Paper]
    • Arnaud Dapogny, Kevin Bailly, and Severine Dubuisson, Pairwise Conditional Random Forests for Facial Expression Recognition, ICCV 2015

Interpretability, regularization, compression pruning and feature selection

  • Global Refinement of Random Forest [[Paper] (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ren_Global_Refinement_of_2015_CVPR_paper.pdf)]
    • Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun, Global Refinement of Random Forest, CVPR 2015
  • L1-based compression of random forest models Arnaud Joly, Fran¸cois Schnitzler, Pierre Geurts and Louis Wehenkel ESANN 2012 [Paper]
  • Feature-Budgeted Random Forest [[Paper] (http://jmlr.org/proceedings/papers/v37/nan15.pdf)] [Supp]
    • Feng Nan, Joseph Wang, Venkatesh Saligrama, Feature-Budgeted Random Forest, ICML 2015
    • Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama NIPS 2016 [Paper]
  • Meinshausen, Nicolai. "Node harvest." The Annals of Applied Statistics 4.4 (2010): 2049-2072. [Paper] [Code R] [Code Python]
  • Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach S. Hara, K. Hayashi, [Paper] [Code]
  • 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). [Paper]

Maintainers - Jiwon KimJung Kwon Lee

原文:https://github.com/kjw0612/awesome-random-forest