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Awesome Random Forest

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, email Jung Kwon Lee ([email protected]) or join our chats to add links.

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)

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

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

Maintainers - Jiwon Kim, Janghoon Choi, Jung Kwon Lee

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Random Forest - a curated list of resources regarding random forest

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