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Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks. (arXiv:1909.11799v5 [cs.LG] UPDATED)
Sept. 8, 2022, 1:13 a.m. | Adam Li, Ronan Perry, Chester Huynh, Tyler M. Tomita, Ronak Mehta, Jesus Arroyo, Jesse Patsolic, Benjamin Falk, Joshua T. Vogelstein
stat.ML updates on arXiv.org arxiv.org
Decision forests (Forests), in particular random forests and gradient
boosting trees, have demonstrated state-of-the-art accuracy compared to other
methods in many supervised learning scenarios. In particular, Forests dominate
other methods in tabular data, that is, when the feature space is unstructured,
so that the signal is invariant to a permutation of the feature indices.
However, in structured data lying on a manifold (such as images, text, and
speech) deep networks (Networks), specifically convolutional deep networks
(ConvNets), tend to outperform Forests. …
More from arxiv.org / stat.ML updates on arXiv.org
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