Functional gradient descent for n-tuple regression

doi:10.1016/j.neucom.2022.05.114

2022-08-01

PRE

Rafael F. Katopodis *

Priscila M. V. Lima

Felipe M. G. França

DOI：10.1016/j.neucom.2022.05.114

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摘要

En 中文

n-tuple neural networks have recently been applied to a wide range of learning domains. However, for the particular area of regression, existing systems have displayed two shortcomings: little flexibility in the objective function being optimized and an inability to handle nonstationarity in an online learning setting. A novel n-tuple system is proposed to address these issues. The new architecture leverages the idea of functional gradient descent, drawing inspiration from its use in kernel methods. Furthermore, its capabilities are showcased in reinforcement learning tasks, which involves both nonstationary online learning and task-specific objective functions.(c) 2022 Elsevier B.V. All rights reserved.

Keyword:

Weightless neural networks

Kernel machines

Reinforcement learning

n-tuple regression

期刊

Neurocomputing

IF:

6.5

论文数:

2.5W

被引数:

6.5W

机构

Universidade Federal do Rio de Janeiro

学者数:

2.9W

论文数: 1.8W

被引数: 1.6W

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