File:NeedForDeeperLayers.svg
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Captions
Captions
A decision tree allows to describe any partition of space and can thus model any decision boundary. Mapping the tree into a one-layer network is possible. Yet, there still is significant residual error in the resulting function.
Summary
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| Description |
English: Figure 3 from Andreas Maier, Christopher Syben. Tobias Lasser. Christian Riess. "A gentle introduction to deep learning in medical image processing". Zeitschrift für Medizinische Physik
Volume 29, Issue 2, May 2019, Pages 86-101. https://www.sciencedirect.com/science/article/pii/S093938891830120X#fig0010 Please reference this article, if you reuse this figure. Original Caption: A decision tree allows to describe any partition of space and can thus model any decision boundary. Mapping the tree into a one-layer network is possible. Yet, there still is significant residual error in the resulting function. In the center example, ϵ ≈ 0.7. In order to reduce this error further, a higher number of neurons would be required. If we construct a network with one node for every inner node in the first layer and one node for every leaf node in the second layer, we are able to construct a network that results in ϵ = 0. |
| Date | |
| Source | https://www.sciencedirect.com/science/article/pii/S093938891830120X#fig0010 |
| Author | Andreas Maier |
Licensing
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This file is licensed under the Creative Commons Attribution 4.0 International license.
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 16:23, 13 October 2019 | | 834 × 547 (692 KB) | AKMaier (talk | contribs) | User created page with UploadWizard |
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