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2015
Springer

Learning with Deep Cascades

4 years 10 months ago
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.
Giulia DeSalvo, Mehryar Mohri, Umar Syed
Added 15 Apr 2016
Updated 15 Apr 2016
Type Journal
Year 2015
Where ALT
Authors Giulia DeSalvo, Mehryar Mohri, Umar Syed
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