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![A Study of Ensemble Learning with AdaBoost and NEAT için kapak resmi A Study of Ensemble Learning with AdaBoost and NEAT için kapak resmi](/client/assets/d79c3e4af2b6d196/ctx/images/no_image.png)
A Study of Ensemble Learning with AdaBoost and NEAT
Başlık:
A Study of Ensemble Learning with AdaBoost and NEAT
Yazar:
Schukei, Robert, author.
ISBN:
9780438071902
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (111 pages)
Genel Not:
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisors: Blayne Mayfield Committee members: Hokwok Dai; Johnson Thomas; Lan Zhu.
Özet:
Neural networks, ensemble algorithms, neuroevolution, and genetic algorithms all have shown the ability to solve classification problems in many areas of application. One of the problems in training neural networks is the significant amount of time the training can take. AdaBoost (i.e., Adaptive Boosting) is an algorithm that has been combined with other neural network training algorithms to form ensemble algorithms that have the goal of reducing training times, while retaining the accuracy level of neural network outputs. Another concern with neural networks is that it can be difficult to determine an effective topology for solving particular problems. Neuroevolution can be used to address this issue; neuroevolution uses genetic algorithms to evolve characteristics of a neural network, one of which is its topology. The focus of this study is to investigate whether AdaBoost can be combined with the genetic algorithms of neuroevolution to decrease the time needed to evolve neural networks, and what effects this would have on the accuracy of the results.
Notlar:
School code: 0664
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(677969.1) | 677969-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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