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Wyszukujesz frazę "Ensemble Learning" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Designing Smart Antennas Using Machine Learning Algorithms
Autorzy:
Samantaray, Barsa
Das, Kunal Kumar
Roy, Jibendu Sekhar
Powiązania:
https://bibliotekanauki.pl/articles/27312957.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
artificial neural network
decision tree
ensemble algorithm
machine learning
smart antenna
support vector machine
Opis:
Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 4; 46--52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Imitation learning of car driving skills with decision trees and random forests
Autorzy:
Cichosz, P.
Pawełczak, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/329901.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
imitation learning
behavioral cloning
model ensemble
random forest
control
autonomous driving
car racing
decision tree
drzewo decyzyjne
lasy losowe
sterowanie
wyścigi samochodowe
Opis:
Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 579-597
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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