Multi-Branch Multi Layer Perceptron: A Solution for Precise Regression using Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148281" target="_blank" >RIV/00216305:26220/23:PU148281 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10109076" target="_blank" >https://ieeexplore.ieee.org/document/10109076</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA57919.2023.10109076" target="_blank" >10.1109/RADIOELEKTRONIKA57919.2023.10109076</a>
Alternative languages
Result language
angličtina
Original language name
Multi-Branch Multi Layer Perceptron: A Solution for Precise Regression using Machine Learning
Original language description
The problem of simple regression using Multi Layer Perceptron (MLP) has its limitations. The main problem with MLP is that it is difficult to find a perfect architecture to fit all the data. The more complex and multi-dimensional the data we use, the deeper the network has to be, which increases training time as well as optimization and tuning of the network. The solution to fit the data more precisely could be to split the data into groups based on the input variable and use a different model to train and predict data for each of these groups. In most cases, datasets contain a large number of non-linear input features, which makes the method for finding thresholds for each group very difficult. The proposed technique tackles this problem using multiple branches consisting of shallow MLP, one of which acts as a selector (classifier) for the output. The selector's main goal is to generate a weight for the other prediction branches which in other words means, results in one branch being more proficient in predicting certain parts of the feature space while the other branches will be better at predicting completely different parts of the feature space.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
RADIOELEKTRONIKA 2023: 2023 33rd International Conference Radioelektronika
ISBN
979-8-3503-9834-2
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
„“-„“
Publisher name
IEEE
Place of publication
NEW YORK
Event location
Pardubice
Event date
Apr 19, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000990505700049