Learning with Deep Cascades
Abstract
We introduce a broad learning model formed by cascades of predictors, Deep Cascades , that is structured as general decision trees in which leaf predictors or node questions may be members of rich function families. We present new data-dependent theoretical guarantees for learning with Deep Cascades with complex leaf predictors and node questions in terms of the Rademacher complexities of the sub-families composing these sets of predictors and the fraction of sample points reaching each leaf that are correctly classified. These guarantees can guide the design of a variety of different algorithms for deep cascade models and we give a detailed description of two such algorithms. Our second algorithm uses as node and leaf classifiers SVM predictors and we report the results of experiments comparing its performance with that of SVM combined with polynomial kernels.
Cite
Text
DeSalvo et al. "Learning with Deep Cascades." International Conference on Algorithmic Learning Theory, 2015. doi:10.1007/978-3-319-24486-0_17Markdown
[DeSalvo et al. "Learning with Deep Cascades." International Conference on Algorithmic Learning Theory, 2015.](https://mlanthology.org/alt/2015/desalvo2015alt-learning/) doi:10.1007/978-3-319-24486-0_17BibTeX
@inproceedings{desalvo2015alt-learning,
title = {{Learning with Deep Cascades}},
author = {DeSalvo, Giulia and Mohri, Mehryar and Syed, Umar},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {2015},
pages = {254-269},
doi = {10.1007/978-3-319-24486-0_17},
url = {https://mlanthology.org/alt/2015/desalvo2015alt-learning/}
}