Robust Multilayer Perceptrons: Robust Loss Functions and Their Derivatives

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00524790" target="_blank" >RIV/67985807:_____/20:00524790 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-48791-1_43" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-48791-1_43</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-48791-1_43" target="_blank" >10.1007/978-3-030-48791-1_43</a>

Result language

angličtina

Original language name

Robust Multilayer Perceptrons: Robust Loss Functions and Their Derivatives

Original language description

Common types of artificial neural networks have been well known to suffer from the presence of outlying measurements (outliers) in the data. However, there are only a few available robust alternatives for training common form of neural networks. In this work, we investigate robust fitting of multilayer perceptrons, i.e. alternative approaches to the most common type of feedforward neural networks. Particularly, we consider robust neural networks based on the robust loss function of the least trimmed squares, for which we express formulas for derivatives of the loss functions. Some formulas, which are however incorrect, have been already available. Further, we consider a very recently proposed multilayer perceptron based on the loss function of the least weighted squares, which appears a promising highly robust approach. We also derive the derivatives of the loss functions, which are to the best of our knowledge a novel contribution of this paper. The derivatives may find applications in implementations of the robust neural networks, if a (gradient-based) backpropagation algorithm is used.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

ISBN

978-3-030-48790-4

ISSN

2661-8141

e-ISSN

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Number of pages

12

Pages from-to

546-557

Publisher name

Springer

Place of publication

Cham

Event location

Halkidiki

Event date

Jun 5, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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