Name
Abstract | ||
|---|---|---|
Randomized classification trees are among the most popular machine learning tools and found successful applications in many areas. Although this classifier was originally designed as offline learning algorithm, there has been an increased interest in the last years to provide an online variant. In this paper, we propose an online learning algorithm for classification trees that adheres to Bayesian principles. In contrast to state-of-the-art approaches that produce large forests with complex trees, we aim at constructing small ensembles consisting of shallow trees with high generalization capabilities. Experiments on benchmark machine learning and body part recognition datasets show superior performance over state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.432 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Offline learning,Online learning,Online machine learning,Naive Bayes classifier,Active learning (machine learning),Pattern recognition,Computer science,Artificial intelligence,Generalization error,Classifier (linguistics),Machine learning,Bayesian probability | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Rota Bulò | 1 | 564 | 33.69 |
| Peter Kontschieder | 2 | 376 | 21.10 |