Distributed decision trees

Name: Distributed decision trees
Author: Irsoy, O., Alpaydın, Ahmet İbrahim Ethem

İsim	Distributed decision trees
Yazar	Irsoy, O., Alpaydın, Ahmet İbrahim Ethem
Basım Tarihi:	2022
Basım Yeri	- Springer
Konu	Decision trees, Hierarchical mixture of experts, Local vs distributed representations
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-303123027-1
Kayıt Numarası	6f2e6e2d-6d09-4ce7-afe2-cf9b610dfc2a
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	In a budding tree, every node is part internal node and part leaf. This allows representing the tree in a continuous parameter space and training it with backpropagation, like a neural network. Unlike a traditional tree whose construction is composed of two distinct stages of growing and pruning, “bud” nodes grow into subtrees or are pruned back dynamically during learning. In this work, we extend the budding tree and propose the distributed tree where the children use different and independent splits; hence, multiple paths in a tree can be traversed at the same time. In a traditional tree, the learned representations are local, that is, activation makes a soft selection among all the root-to-leaf paths in a tree, but the ability to combine multiple paths of the distributed tree gives it the power of a distributed representation, as in a traditional perceptron layer. Our experimental results show that distributed trees perform comparably or better than budding and traditional hard trees.
DOI	10.1007/978-3-031-23028-8_16
Cilt	13813

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Distributed decision trees

Yazar Irsoy, O., Alpaydın, Ahmet İbrahim Ethem

Basım Tarihi 2022

Basım Yeri - Springer

Konu Decision trees, Hierarchical mixture of experts, Local vs distributed representations

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-303123027-1

Kayıt Numarası 6f2e6e2d-6d09-4ce7-afe2-cf9b610dfc2a

Lokasyon Computer Science

Tarih 2022

Örnek Metin In a budding tree, every node is part internal node and part leaf. This allows representing the tree in a continuous parameter space and training it with backpropagation, like a neural network. Unlike a traditional tree whose construction is composed of two distinct stages of growing and pruning, “bud” nodes grow into subtrees or are pruned back dynamically during learning. In this work, we extend the budding tree and propose the distributed tree where the children use different and independent splits; hence, multiple paths in a tree can be traversed at the same time. In a traditional tree, the learned representations are local, that is, activation makes a soft selection among all the root-to-leaf paths in a tree, but the ability to combine multiple paths of the distributed tree gives it the power of a distributed representation, as in a traditional perceptron layer. Our experimental results show that distributed trees perform comparably or better than budding and traditional hard trees.

DOI 10.1007/978-3-031-23028-8_16

Cilt 13813