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End-to-end Learning of Deterministic Decision Trees
[article]
2017
arXiv
pre-print
Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scratch those features that best allow to solve a given supervised learning problem. Recent work (Kontschieder 2015) has addressed this deficit, but at the cost of losing a main
arXiv:1712.02743v1
fatcat:vbebddzmdnbhnb7jxvyguoie3i