國立虎尾科技大學 |

A Study of Ensemble Learning with AdaBoost and NEAT.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A Study of Ensemble Learning with AdaBoost and NEAT./
作者:	Schukei, Robert.
面頁冊數:	1 online resource (111 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438071902

A Study of Ensemble Learning with AdaBoost and NEAT.
Schukei, Robert.

A Study of Ensemble Learning with AdaBoost and NEAT. - 1 online resource (111 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--Oklahoma State University, 2017.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438071902Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

A Study of Ensemble Learning with AdaBoost and NEAT.
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504 $a Includes bibliographical references
520 $a 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.
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