@inproceedings{c402219608a949e2a4c3c624a2f034d5,
title = "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.",
keywords = "Decision trees, Learning theory, Supervised learning",
author = "Giulia DeSalvo and Mehryar Mohri and Umar Syed",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 26th International Conference on Algorithmic Learning Theory, ALT 2015 ; Conference date: 04-10-2015 Through 06-10-2015",
year = "2015",
doi = "10.1007/978-3-319-24486-0_17",
language = "English (US)",
isbn = "9783319244853",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "254--269",
editor = "Claudio Gentile and Sandra Zilles and Kamalika Chaudhuri",
booktitle = "Algorithmic Learning Theory - 26th International Conference, ALT 2015",
}