Machine Learning
Shape quantization and recognition with randomized trees
Neural Computation
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
Machine Learning
Limiting the Number of Trees in Random Forests
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
An introduction to boosting and leveraging
Advanced lectures on machine learning
Comparison of Genetic Algorithm and Sequential Search Methods for Classifier Subset Selection
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Robust Boosting for Learning from Few Examples
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Machine Learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A two-stage outlier rejection strategy for numerical field extraction in handwritten documents
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Random Forests for Handwritten Digit Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Evidence Contrary to the Statistical View of Boosting
The Journal of Machine Learning Research
Forest-RK: A New Random Forest Induction Method
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Influence of Hyperparameters on Random Forest Accuracy
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
An incremental extremely random forest classifier for online learning and tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic integration with random forests
ECML'06 Proceedings of the 17th European conference on Machine Learning
Dynamic random regression forests for real-time head pose estimation
Machine Vision and Applications
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In this paper, we introduce a new Random Forest (RF) induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure. The main idea is to guide the tree induction so that each tree will complement as much as possible the existing trees in the ensemble. This is done here through a resampling of the training data, inspired by boosting algorithms, and combined with other randomization processes used in traditional RF methods. The DRF algorithm shows a significant improvement in terms of accuracy compared to the standard static RF induction algorithm.