Incremental Learning of Random Forests for Large-Scale Image Classification
By Marko Ristin, Matthieu Guillaumin, Juergen Gall, Luc Van Gool. 2016
148 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Fei Sha
- Wei-Lun Chao
- Bernt Schiele
- Soravit Changpinyo
- Han-Jia Ye
- De-chuan Zhan
- Zeynep Akata
- Cordelia Schmid
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Bernt Schiele
- Xilin Chen
- Trevor Darrell
- Xiaogang Wang
- Cordelia Schmid
- Fei Sha
In Meta & Continual Learning
- Svetlana Lazebnik
- Wei-Lun Chao
- Shiguang Shan
- Fei Sha
- Jeffrey O. Zhang
- Soravit Changpinyo
- Nivedita Mishra
- Ruiping Wang
Citations in the corpus
- Memory-Efficient Incremental Learning Through Feature Adaptation (2020, background): “The literature for incremental learning prior to the deep-learning era includes incrementally trained support vector machines [7], random forests [42], and metric-based methods that generalize to new classes [36].”
- Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review (2021, methodology): “Incremental Learning is often used in image classification [116], target recognition [117], and used less in Intrusion Detection or”
- Class Boundary Exemplar Selection Based Incremental Learning for Automatic Target Recognition (2020, methodology): “Incremental random forest [17] is an ensemble strategy, which adds nodes of its decision subtrees and increases the number of the subtree.”
- Exemplar-Supported Generative Reproduction for Class Incremental Learning (2018, background): “Before deep learning era, there are various incremental learning approaches like incremental SVMs [6], ensemble methods [25, 28], distance-based methods [21] and so on.”
- Under review as a conference paper at ICLR 2020 many domain adaptation methods (2019, methodology): “(2) Conventionally, the simple split functions that compares only a couple of feature values are used, but recently more elaborate split functions using the linear classifiers are used Yao et al. (2011); Ristin et al. (2015b).”
- Complete Random Forest Based Class Noise Filtering Learning for Improving the Generalizability of Classifiers (2019, background): “In addition, the feature voting mechanism in the CRF can balance and reduce the effects of feature noise in highdimensional data, and this has been demonstrated in RFs in recent years [59], [60].”
- Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning (2016, background): “Finally, the main focus of [24] is on incremental, not zero-shot, learning settings (see also [34, 31]).”