Deep Forest as a framework for a new class of machine-learning models

被引：0

作者：

Lev V.Utkin ^{[1
]}

Anna A.Meldo ^{[1
]}

Andrei V.Konstantinov ^{[1
]}

机构：

[1] Laboratory of Neural Network Technology and Artificial Intelligence, Peter the Great St. Petersburg Polytechnic University

来源：

National Science Review | 2019年 / 6卷 / 02期

基金：

俄罗斯科学基金会;

关键词：

Deep Forest as a framework for a new class of machine-learning models;

D O I：

暂无

中图分类号：

TP181 [自动推理、机器学习];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new deep learning framework—the socalled Deep Forest (DF), proposed by Zhi-Hua Zhou and Ji Feng [1,2]—can be regarded as one of the important events of 2017 in machine learning, although it was unjustly unnoticed by a large number of researchers. The DF combines several ensemble-based methods, including Random Forests (RFs) and Stacking, into a structure that is similar to a multi-layer neural network, but each layer in the DF

引用

页码：186 / 187

页数：2

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