On-line Random Forests

By Amir Saffari, Christian Leistner, Jakob Santner, Martin Godec, Horst Bischof. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009

562 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7633c95812dea716917e8b23b84df18e7b03614e

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In Computer Vision and Pattern Recognition

Citations in the corpus

Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China (2020, background): “Due to their high computational efficiency in both training and evaluation, in addition to their ability to achieve state-of-the-art results, random forests (RF) are frequently used in text classification [21].”
Uniform Convergence of Random Forests via Adaptive Concentration (2015, background): “Notable extensions of the original random forest algorithm include online random forests [15, 34], random survival forests [23] and Bayesian alternatives to random forests [13].”
Random forests: from early developments to recent advancements (2014, methodology): “Saﬀari, Leistner, Santner, Godec, and Bischof (2009) proposed a novel online RF algorithm to remedy the limitations of the oﬀ-line algorithm which has limited usability for many practical problems.”
Tracking-Learning-Detection (2012, methodology): “6.2 Comparison 2: Prost TLD was compared with the results reported in [67] which reports on performance of five algorithms (Online Boosting (OB) [29], Online Random Forrest (ORF) [68], Fragmentbaset Tracker (FT) [20], MIL [30], and Prost [67]) on 10 benchmark sequences.”
Ensemble learning for data stream analysis: A survey (2017, methodology): “Bagging-based OzaBag [137] Online Bagging ASHT [17] Ensemble of adaptive-size Hoeffding trees LevBag [16] Leveraging Bagging with increased resampling and output detection codes ORF [41,153] Online Random Forest MF [111] Online Mondrian Forest Boosting-based OzaBoost [137] Online Boosting Others UFF”
Consistency of Online Random Forests (2013, methodology): “Many of the online techniques incorporate features designed for this problem (Gama et al., 2005; Abdulsalam, 2008; Saﬀari et al., 2009; Bifet et al., 2009; 2012).”
Ensemble learning: A survey (2018, background): “Random forest also has a real-time version (Saffari, Leistner, Santner, Godec, & Bischof, 2009) which is capable of addressing concept drift in sequential data.”
Random Forests for Big Data (2015, background): “Key words: Random Forest, Big Data, Parallel Computing, Bag of Little Bootstraps, On-line Learning”
Hough-based tracking of non-rigid objects (2011, methodology): “[28] transform Random Forests to the online learning domain.”
Incremental Learning of NCM Forests for Large-Scale Image Classification (2014, background): “[9, 18, 25] lazily split the nodes or destroy the (sub)tree after a sufﬁ-cient number of samples have been observed or a certain threshold on information gain has been passed.”
Place categorization and semantic mapping on a mobile robot (2015, methodology): “Recently [7] used ConvNets to beat all competing approaches on the task of semantic place categorization.”
Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2011, unknown): “The work in [77] presents further details.”
Recent advances and trends in visual tracking: A review (2011, methodology): “Many state-of-the-art discriminative online learning based tracking methods [85,87–89, 93”
Incremental Learning of Random Forests for Large-Scale Image Classification (2016, background): “[9, 18, 25] lazily split the nodes or destroy the (sub)tree after a sufficient number of samples have been observed or a certain threshold on information gain has been passed.”